1. Gutachter: Prof. Dr. Rolf Chini; Bochum 2. Gutachter: Prof. Dr. Ralph Neuhäuser; Jena

Datum der Disputation: 17.09.2010

VARIABILITY OF

YOUNG STARS

PHD THESIS

FACULTY OF PHYSICS AND ASTRONOMY

RUHR-UNIVERSITY BOCHUM

CLAUS-MICHAEL SCHEYDA

BOCHUM

BOCHUM 2010

First referee: Prof. Dr. Rolf Chini; Bochum Second referee: Prof. Dr. Ralph Neuhäuser; Jena

Date of defense: 09/17/2010

For my father

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TABLE OF CONTENTS

Chapter I – Introduction & Overview ............................................................................................... 1

1.1 Star Formation .............................................................................................................. 1

1.2 Variable Young Stellar Objects ............................................................................... 2

1.2.1 TTauri Stars ..................................................................................................... 2

1.2.2 HAeBe Stars ..................................................................................................... 2

1.3 The Omega Nebula ...................................................................................................... 3

1.4 This Thesis ...................................................................................................................... 4

Chapter II – Observation & Data Reduction .................................................................................. 5

2.1 Observations .................................................................................................................. 5

2.1.1 Sutherland, South Africa............................................................................. 5

2.1.2 Calar Alto, Spain ............................................................................................. 8

2.2 Data Reduction ............................................................................................................ 11

2.2.1 IRSF Data ........................................................................................................ 12

2.2.2 Calar Alto Data .............................................................................................. 12

Chapter III – Data Analysis ................................................................................................................. 15

3.1 Image Subtraction: ISIS ........................................................................................... 16

3.1.1 ISIS Operation Overview .......................................................................... 16

3.1.2 ISIS Analysis Steps ...................................................................................... 17

3.2 Calibration: IRAF & 2MASS .................................................................................... 24

3.3 Lightcurve Analysis ................................................................................................... 26

Chapter IV – Results & Interpretation ........................................................................................... 29

4.1 Types of Young Variables ....................................................................................... 29

4.1.1 Low Mass Young Variables ...................................................................... 29

4.1.2 Intermediate Mass Young Variables .................................................... 35

4.1.3 Other Types of Variables .......................................................................... 36

4.2 Statistics & Lightcurves ........................................................................................... 37

4.2.1 Statistics Summary ..................................................................................... 37

4.2.2 Template Lightcurves ................................................................................ 48

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4.3 Analysis-Diagrams over Time .............................................................................. 55

4.3.1 Color-Color Diagrams ............................................................................... 55

4.3.2 Color-Magnitude Diagrams .................................................................... 57

4.4 Comparison to Other Studies ............................................................................... 61

4.4.1 IR-Excess and CO-Features ..................................................................... 62

4.4.2 X-Ray Emission ............................................................................................ 65

4.4.3 Polarization ................................................................................................... 65

Chapter V – Summary & Outlook ..................................................................................................... 69

5.1 Variability as a Tracer for Star Formation ...................................................... 69

5.2 The IRIS Project ......................................................................................................... 70

Chapter VI – Acknowledgements .................................................................................................... 71

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LIST OF FIGURES

Figure I—1: Three color composite of M 17 at optical wavelengths .................................. 3 Figure II—1: The South African Astronomical Observatory .................................................. 6 Figure II—2: Transmissioncurve IRSF J, H, and Ks ..................................................................... 6 Figure II—3: Schematics of the IRSF SIRIUS-camera ................................................................ 7 Figure II—4: The Astronomical Center at Calar Alto and the 3.5m telescope ................ 9 Figure II—5: The OMEGA2000 camera mounted on the telescope..................................... 9 Figure II—6: Erroneous flux subtraction on CAHA image .................................................... 14 Figure III—1: Flow chart displaying ISIS analysis steps ........................................................ 18 Figure III—2: Erroneous background computation using tiles on CAHA image ......... 19 Figure III—3: Comparison variability image to reference image ....................................... 21 Figure III—4: Noise on the variability image ............................................................................. 22 Figure III—5: 2MASS reference stars used in this study ....................................................... 25 Figure III—6: Template Gri-plot ...................................................................................................... 27 Figure IV—1: V-band variability over time diagram of a typical Classical TTauri ...... 30 Figure IV—2: V-band magnitude vs. phase diagram of a typical Weak-line TTauri .. 31 Figure IV—3: Doppler imaging illustrative of star-spots on a Weak-line TTauri........ 32 Figure IV—4: Template lightcurves of FU Orionis outbursts .............................................. 33 Figure IV—5: V-band photometry of EX Lupi ............................................................................. 34 Figure IV—6: Small-scale HAeBe variability ............................................................................... 35 Figure IV—7: Lightcurve and color-variation of UX Orionis ................................................ 36 Figure IV—8: IRSF field of view superposed on CAHA field of view................................. 38 Figure IV—9: Variable stars per magnitude – combined K .................................................. 39 Figure IV—10: Rotational period comparison (incl. IR-excess stars) .............................. 41 Figure IV—11: Variable stars per magnitude – JHK (M 17 core) ....................................... 41 Figure IV—12: Average variability depending on reference magnitude ........................ 42 Figure IV—13: Distribution of variability amplitudes ............................................................ 44 Figure IV—14: Distribution of variability periods ................................................................... 44 Figure IV—15: Time between observations ............................................................................... 45 Figure IV—16: Variables distribution for inner and outer M 17 ........................................ 46 Figure IV—17: Variability period comparison inner and outer M17 ............................... 47 Figure IV—18: Variability amplitude comparison inner and outer M17 ........................ 47 Figure IV—19: Comparison of CAHA and IRSF lightcurves of star lc150 ....................... 52 Figure IV—20: Comparison of CAHA and IRSF lightcurve of star lc395 ......................... 52 Figure IV—21: Comparison of lightcurves in three filters .................................................... 53 Figure IV—22: Comparison of lightcurves derived from IRSF J, H, K, and CAHA K .... 54 Figure IV—23: Two color diagram – (J-H) vs. (H-K) ................................................................ 56 Figure IV—24: Two color diagram – (J-H) vs. (H-K) with color variations .................... 56

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Figure IV—25: Color-magnitude diagram – J vs. (H-K) .......................................................... 58 Figure IV—26: Color-magnitude diagram – K vs. (H-K) ........................................................ 58 Figure IV—27: Color-magnitude diagram – K vs. (B-K) ......................................................... 60 Figure IV—28: Detection conformity comparison to other studies – overall ................. 61 Figure IV—29: Detection conformity comparison to other studies – per magnitude . 62 Figure IV—30: Comparison of variability amplitude with archival data ....................... 63 Figure IV—31: Distribution of variable stars showing IR-excess...................................... 64 Figure IV—32: Distribution of variable stars showing CO-bands ..................................... 64 Figure IV—33: Distribution of variable stars showing X-ray emission .......................... 66 Figure IV—34: Distribution of variable stars showing polarization ................................ 66

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LIST OF TABLES

Table II—1: Positioning and dithering details for IRSF observations ................................ 7 Table II—2: Overview of observation conditions at SAAO 2008 ......................................... 8 Table II—3: Overview of observation conditions at Calar Alto 2008 ............................... 10 Table II—4: Overview of observation conditions at Calar Alto 2009 ............................... 11 Table III—1: Parameters for ISIS interp.csh script ............................................................ 19

Table III—2: Parameters for ISIS ref.csh script .................................................................... 20 Table III—3: Parameters for ISIS subtract.csh script ....................................................... 20

Table III—4: Parameters for ISIS detect.csh script ............................................................ 21 Table III—5: Parameters for ISIS find.csh script.................................................................. 22

Table III—6: Parameters for ISIS phot.csh script.................................................................. 23 Table III—7: 2MASS reference stars used in this study ......................................................... 24 Table III—8: Parameters for IRAF noao.digiphot.daophot.phot task ................... 26 Table III—9: Parameters for ISIS czerny routine ................................................................... 26

Table IV—1: Overview of variable stars per dataset ............................................................... 37 Table IV—2: Template lightcurves ................................................................................................. 49 Table IV—3: Template lightcurves (cont.) ................................................................................... 50 Table IV—4: Template lightcurves (cont.) ................................................................................... 51

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ABSTRACT

This thesis investigates the use of infrared variability surveys on the study of young stellar objects. Although many variability studies have been performed in visual light, the dense nature of star forming regions severely limits the penetration at op-tical wavelengths. Analysis of 2MASS archival data by Carpenter, et al. showed the significance of infrared observations. The observations contained herein represent the first dedicated variability study towards a young star forming region like M 17.

Using a combination of frequent three filter observations (J, H, and K) over the course of one month at the 1.4 m infrared telescope at the Sutherland Observatory, South Africa, and long term (over a year) observations with the 3.5 m telescope at the Calar Alto Observatory, Spain, allowed an unparalleled survey of a nearby H II region. 8000 stars were measured at more than 40 epochs, revealing about 10% of variable stars.

In the analysis of the datasets variability amplitudes from 0.01 mag to over 2 mag were found. Period detection algorithms derived recurring phenomena with periods from hours up to months and more. The inclusion of simultaneous observations in three filters allowed the calculations of color variability of up to 2.5 mag. The dis-covery of these color variations reveals important ambiguities in the—very com-mon—use of color-color diagrams for stellar classifications using only single epoch data.

Various types of low and intermediate mass young stellar objects, like TTauri stars and HAeBe stars, are discussed. Potential candidates of eruptive variables, like FU Orionis stars, are identified in template lightcurves.

The results of this variability survey are checked against other studies towards M 17. Infrared excess emission photometry, spectroscopy to detect CO-bands, X-ray observations, and polarization studies are compared qualitatively and quantitative-ly. The comparison shows that variability studies are the most versatile and thor-ough tool to investigate stellar youth on grand scales.

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Chapter I – INTRODUCTION & OVERVIEW

This chapter introduces models of star formation, types of Young Stellar Objects, and the Omega Nebula (M 17) and describes the contents of this thesis.

1.1 STAR FORMATION

The current picture we see, when looking at the universe would be completely dif-ferent without continuing formation of stars. It is probably the most important building block for the universe. Without ongoing star formation all galaxies and the universe itself would be dark—all stars formed in the beginning of the universe would have burned out. This is evidenced in the fact, that no Population III stars were ever found in the current universe (there are—however—some recent candi-dates in galaxies at z=3.5, but those are from a very long time ago … (Fosbury, et al., 2003)).

Although low-mass star formation is well understood (see for example (Bally, et al., 2006)) and recent discoveries by Chini, et al. (Chini, et al., 2004) further sharpened the picture of the building of high-mass stars, a larger sample of young stars in all their different stages is still needed.

There are two main problems in finding enough young stars to study their formation in detail. First, the phase of star formation is rather short (a few million years, highly dependent on mass), compared to the life span of a star (except for very massive stars up to billions of years). The probability of observing an event is, among other factors, proportional to its duration. This means, that one is more likely to observe developed stars than young stars. The second problem lies in the nature of star for-mation itself, which takes place in dense regions of space. That means high concen-trations of dust and interstellar matter, resulting in a highly obscured cradle which is difficult to observe.

Chapter I – Introduction & Overview

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1.2 VARIABLE YOUNG STELLAR OBJECTS

When systematically looking for Young Stellar Objects (henceforth “YSOs”), a meth-od has to be found, which allows surveying large areas at the sky and detecting young stars. In the days of photographic plates, which had a huge field of view, lens-prisms could be used to highlight prominent Hα lines—common to certain types of young stars—in a star field (see for example (Wilking, et al., 1987)). Other means of detection are excess emission from heated dust shells at near infrared wavelengths, absorption or emission lines of 13CO molecules in the spectra, and variability. Ac-cording to Carpenter, et al. (Carpenter, et al., 2001), the most useful index for an YSO is to detect variability, because of the fierce development from protostar to the main-sequence.

Depending on mass and age there are a variety of sub-classes of YSOs—those are introduced briefly in the following and discussed in-depth in Chapter 4.1.

1.2.1 TTAURI STARS

For stellar progenitors of low masses (below 2 M⊙1) the early phases of star for-mation are dominated by the TTauri phase (class II and III objects), named after the star TTauri, where this phenomenon was first observed (Joy, 1945). This type of young stars is further divided into Classical TTauris, which flicker due to irregular infall of disk-material and exhibit small variability on a timescale of days, and Weak-line TTauris, who show a variability of approximately half a magnitude over weeks, caused by giant star-spots.

The eruptive variable classes of FU Orionis and EX Lupi stars (or Fuors and Exors for short) are also special types of TTauri stars. Fuors have—probably recurring—outbursts, which can increase their brightness a hundredfold. Exors show a repeti-tive variability of up to four magnitudes over a period of months. For further infor-mation about TTauri stars, see Chapter 4.1.1.

1.2.2 HAEBE STARS

The analogon to TTauri stars in the intermediate mass regime (up to 8 M⊙) are Herbig Ae/Be2 stars (HAeBe). These pre-main-sequence stars are often surrounded by a TTauri cluster and display highly variable emission lines originating in their hot and turbulent chromospheres. An eruptive sub-class of HAeBe stars is formed by UX Orionis stars, which display an erratic variability of approximately one magni-

1 The unit M⊙ = “Solar Masses” refers to the mass of our Sun 2 The “A/B” refers to the spectral type of the stars; the lowercase “e” represents the bright emission lines frequent from various elements (e.g. Hydrogen)

Section 1.3 – The Omega Nebula

3

tude over one day superimposed on a repetitive change in brightness of about four magnitudes over a period of years.

The statistics on all these YSOs is very sparse and a large and unbiased sample is clearly needed. For more information on HAeBe stars, see Chapter 4.1.2.

1.3 THE OMEGA NEBULA

One of the most valuable regions in space for studying star formation is the Ome-ga Nebula, designated Messier object 17, or M 17 for short. M 17 is an H II region in the constellation Sagittarius at 18h 20m 26s right ascension and -16° 10′ 36″ declina-tion (see Figure I—1). Besides the luminous H II region, M 17 consists of a molecular

cloud in the south-west and an embedded very young star cluster of only 5 × 105 years (Chini, et al., 2008). M 17 spans a field of 3.6 × 3.7 square parsec (pc) at a dis-tance of 2.1 kpc, which results in an apparent diameter of 11 arcminutes. This, com-bined with modern detectors and telescopes, enables observations at a resolution, that allows for studying individual (proto-)stars including their disks.

Figure I—1: Three color composite of M 17 at optical wavelengths Copyright 2007 Stefan Heutz & Wolfgang Ries, http://www.astro-cooperation.com

http://www.astro-cooperation.com/

Chapter I – Introduction & Overview

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1.4 THIS THESIS

This thesis presents the first infrared variability survey with dedicated (i.e., not from archival data) three filter observations of a young star forming region over a time span of more than a year. This allowed a deep look in the variability of the star forming region M 17.

Variability is thought to be the most promising method in discovering Young Stellar Objects. To overcome the extinction in dense regions, observations in the infrared3 are needed. In the—very near—future those will be made with a special telescope completely dedicated to a survey of all star forming regions: The IRIS telescope at the Observatory Cerro Armazones (OCA)4.

Before the commissioning of IRIS certain software packages and the method in gen-eral had to be tested. To accomplish this, observations similar to those of IRIS were made over the period of one month at the Sutherland Observatory in South Africa. To evaluate long-term variability, additional observations were performed at the Calar Alto Observatory in Spain. The latter took place over the course of more than a year in changing intervals.

For this thesis all the data was analyzed with the astronomical software packages IRAF5 and ISIS6, different types of variability are visualized as lightcurves using Czerny-Schwarzenberg period analysis algorithms and evaluations of using these methods on future projects are given.

Chapter II contains details on the observations (2.1) and the data reduction process (2.2). Chapter III deals in-depth with the data analysis software (3.1), the magni-tudes calibration (3.2), and the lightcurve creation (3.3). Chapter IV describes the different types of young variables in detail (4.1) and then discusses the results and statistics from various viewpoints (4.2 and 4.3). A comparison to other studies (4.4) concludes this chapter. Chapter V gives a brief summary and outlook on the use of variability surveys as tracers for star formation (5.1) and previews the new IRIS telescope (5.2).

3 In the following sometimes abbreviated as “IR” 4 The OCA is a joined operation by the Astronomisches Institut Ruhr-Universität Bochum and the Instituto Astrofisico Universidad Catolica del Norte Antofagasta. It is located on the Cerro Armazones in the Atacama Desert in Chile. For more information, see the OCA website at http://www.astro.ruhr-uni-bochum.de/astro/oca/index.html. 5 IRAF (Image Reduction and Analysis Facility) is distributed by the National Optical Astron-omy Observatories, which are operated by the Association of Universities for Research in Astronomy, Inc., under cooperative agreement with the National Science Foundation. 6 Image Subtraction package by Christophe Alard (Alard, 2000)

http://www.astro.ruhr-uni-bochum.de/astro/oca/index.html

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Chapter II – OBSERVATION & DATA REDUCTION

In this chapter, details on the observations are given, and the reduction process is explained. All used software packages and their settings are mentioned.

2.1 OBSERVATIONS

For this thesis dedicated observations from the Calar Alto Observatory in Spain and from the Sutherland Observatory in South Africa were used.

2.1.1 SUTHERLAND, SOUTH AFRICA

The South African Astronomical Observatory (SAAO), established 1972 and run by South Africa's National Research Foundation, is the national center for optical infra-red astronomy in South Africa. Located about 400km north-east of Cape Town, near the small town Sutherland in an altitude of 1458m, the observatory consists of five telescopes with apertures of 1.9m, 1.4m, 1.0m, 0.75m, and 0.5m; along with another five robotic telescopes (Figure II—1).

The 1.4m telescope—or InfraRed Service Facility, IRSF—is a joint project between the Nagoya University, the Kyoto University, the National Astronomical Observatory of Japan (NAOJ), and the SAAO. Observations used in this thesis were conducted using the SIRIUS-camera (Simultaneous-3color InfraRed Imager for Unbiased Sur-vey), developed at Nagoya University and NAOJ.

SIRIUS offers simultaneous observation in three Johnson filters: J (at a central wave-length of 1.25µm), H (1.65µm), and Ks7 (2.15µm). See Figure II—2 for details on the filter transmission and Figure II—3 for schematics of the camera. The instrument is

7 For simplicity’s sake the Ks-filter will be designated just K in the following text

Chapter II – Observation & Data Reduction

6

equipped with three 1024 × 1024 HAWAII CCD detectors. Each exposure covers a field of view of 7.7′ × 7.7′ with a resolution of 0.45″ per pixel.

To be able to analyze faint, as well as bright stars, our proposal called for three inte-gration times of 1.6s, 10s, and 30s respectively. The faint stars would not be visible in the short exposure frames, but the bright stars will still be in the linearity range of the detector. The longer exposures allow detection and measurements of fainter stars—the fact that the bright stars will saturate the detector is of no consequence, if the wrongly derived magnitudes of them are not used in the further analysis.

In the infrared regime, additional calibration frames are needed to correct for night-sky emission. The long IR wavelengths allow better penetration of dust clouds—and therefore lower the effective extinction coefficient—but on the downside, emission of molecules in the earth atmosphere has to be taken into account. The near-IR sky background is dominated by many intrinsically narrow hydroxyl (OH) emission

Figure II—1: The South African Astronomical Observatory Copyright 2007 InfraRed Service Facility; the IRSF telescope is the one with the silver dome in the background between the two grey domes in the front

Figure II—2: Transmissioncurve IRSF J, H , and Ks Showing transmission efficiency for the three Johnson filters: J (at a central wave-length of 1.25µm), H (1.65µm), and Ks (2.15µm)

Section 2.1 – Observations

7

lines. In addition, water (H2O) lines contribute at the long wavelength end of the K filter. During the night the emission lines vary 5-10% in brightness on a timescale of 5-15 minutes, as atmospheric wave phenomena change the local density of species.

To compensate for the highly varying background emission, the same set of expo-sures used for the science frames, was observed 30 arcminutes off-fields for sky frames. All observations consisted of multiple exposures, taken using a dithering pattern to filter out cosmic rays and to minimize the effect of faulty pixels on the detector. For details on this, see Table II—1.

Object Frame Sky Frame

Position (RA, Dec., J2000)

18h 20m 28.41s -16° 10′ 09.5″

18h 19m 40.00s -16° 22′ 46.0″

Dithering Throw 15 arcmin 30 arcmin

Dithering repeats, 1.6s Exposure

10 10

Dithering repeats, 10s Exposure

10 10

Dithering repeats, 30s Exposure

25 25

Table II—1: Positioning and dithering details for IRSF observations

Figure II—3: Schematics of the IRSF SIRIUS-camera


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As there is no automatic monitoring of observation conditions at SAAO, the results were carefully checked for anomalies—for example, if on one night an unusual per-centage of stars were in their minimum, or maximum. The seeing is derived from the median of the full-width at half-maximum (henceforth abbreviated FWHM), as measured on the frames and averaged through all filters. The observation data is summarized in Table II—2.

2.1.2 CALAR ALTO, SPAIN

The German-Spanish Astronomical Center at Calar Alto is located in the Sierra de Los Filabres (Andalucía, Southern Spain) north of Almeria. It is operated jointly by the Max-Planck-Institut für Astronomie (MPIA) in Heidelberg, Germany, and the Instituto de Astrofísica de Andalucía (CSIC) in Granada/Spain. Four telescopes with apertures of 1.23m, 1.5m, 2.2m, and 3.5m are located at the summit of the Calar Alto in an altitude of 2168m (Figure II—4).

For this thesis, service mode observations were conducted at the 3.5m telescope, using the OMEGA-2000 near infrared camera (Figure II—5). Located in the prime

Flag Night M FWHM Mean Seeing Conspicuous 09/11/2008 5.3 2.4 yes 09/12/2008 2.8 1.3 no ! 09/12/2008 3.1 1.4 no

! 09/13/2008 3.6 1.6 no

09/16/2008 3.1 1.4 no 09/16/2008 2.8 1.3 yes ! 09/17/2008 2.3 1.0 yes

! 09/17/2008 2.4 1.1 maybe 09/20/2008 4.3 1.9 maybe 09/21/2008 2.7 1.2 no 09/24/2008 3.3 1.5 no 09/25/2008 2.9 1.3 no 09/27/2008 3.4 1.5 no 09/28/2008 2.5 1.1 no ! 09/30/2008 3.9 1.8 maybe

10/01/2008 3.1 1.4 no 10/03/2008 3.4 1.5 no ! 10/04/2008 3.5 1.6 no

! 10/05/2008 3.8 1.7 no

Table II—2: Overview of observation conditions at SAAO 2008

Entries marked “” were not used in the analysis, either because of bad seeing cond i-

tions (larger than 1.8 arcsec), or because the dataset showed a conspicuously high

percentage of stars in their minimum/maximum. Entries marked “!” were used with

caution in the analysis, either because of moderate seeing conditions ( larger than 1.5 arcsec), or because a tendency towards a conspicuously high percentage of min i-ma/maxima was evident. The remaining entries—marked “”—are fully trusted.

Section 2.1 – Observations

9

focus of the telescope, the OMEGA-2000 camera delivers a large (15.4 × 15.4 arcmin²) field of view. The camera is equipped with a 2048 × 2048 pixel HAWAII-2 CCD detector8, delivering a pixel scale of 0.45 arcsec/pixel. Observations were car-ried out using a Johnson Ks-filter9 (central wavelength at 2.151µm, with a full-width at half-maximum (FWHM) of 0.304µm).

8 Rockwell HAWAII2 HgCdTe 2048 x 2048 pixel focal-plane array 9 Again: For simplicity’s sake the Ks-filter will be designated just K in the following text

Figure II—4: The Astronomical Center at Calar Alto and the 3.5m telescope Left: the 3.5m telescope is located in the biggest dome in the background; right: the telescope itself, note the two people for a scale comparison; Copyright 2004 Max-Planck-Institut für Astronomie

Figure II—5: The OMEGA2000 camera mounted on the telescope Copyright 2004 Max-Planck-Institut für Astronomie


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The proposal was as such, that whenever half an hour observing time was left in a night, our observations would be performed. The goal was to check for variability on all timescales, so we wanted a random sample of observing intervals. The result was 17 nights during 2008, and 20 nights during 2009, in which observations were made. For the second half of 2009 we altered the proposal slightly to check for short time variability. Instead of one 30 minute exposure—consisting of 30 one-minute-exposures in a row—we applied for two sets of 15 one-minute-exposures: one taken in the beginning of the night and the other in the end.

The Calar Alto Observatory has an automatic observation condition monitoring sys-tem, which provides weather information (especially on clouds), mean seeing val-ues, and general classification of the photometric conditions.

All observations used a dithering pattern, to account for bad pixels and cosmic ray impacts during exposures. Therefore the resulting field of view is slightly smaller (13.6 × 13.7 arcmin²) than the instruments field of view. Due to the “opportunity” nature of the proposal, the observation of dedicated sky-frames was not possible. It was planned to use a combined average of the non-shifted dithered images to create an artificial sky-frame (more on this topic in Chapter 2.2.2).

Flag Night Usable Images

General Conditions Mean

Seeing

06/17/2008 0 photometric 1.1

06/19/2008 30 photometric 1.0

06/21/2008 29 photometric 0.9

07/10/2008 25 mostly clear, not photometric 1.6

07/14/2008 15 partially cloudy 1.0

07/17/2008 28 photometric 1.0

08/11/2008 30 photometric 1.3

08/12/2008 24 photometric 1.2

! 08/19/2008 30 partially photometric 1.2

08/20/2008 30 photometric 1.0

! 08/21/2008 30 partially cloudy 1.0

! 09/09/2008 22 mostly clear, not photometric 1.1

! 09/10/2008 24 mostly clear, not photometric 0.9

09/15/2008 29 photometric 1.0




Table II—3: Overview of observation conditions at Calar Alto 2008 Entries marked “” were not used in the analysis, because of either bad weather, se e-ing conditions more than 33% outside specifications of 1a s, or the total integration time was below 66% of the standard integration time of 1800s. Entries marked “ !”, indicating only partially photometric conditions, are used with caution in the further analysis. The remaining entries—marked “”—are fully trusted.

Section 2.2 – Data Reduction

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Due to certain constraints we set (see Table II—3 for details), only 7 sets in 2008 and 8 in 2009 were one hundred percent compliant to our standards. An additional 6 datasets in 2008 and 10 in 2009 were observed under partially photometric condi-tions—which in most of the cases meant that on some region of the sky thin cirrus clouds could be seen. On those dates special care was taken interpreting eventually detected variability.

2.2 DATA REDUCTION

In this section, the software—along with the parameters and settings used—and the necessary steps for the preparation of the data for final analysis are explained.

Flag Night Usable Images

General Conditions Mean

Seeing




05/06/2009 30 photometric 0.8

05/07/2009 32 photometric 1.2

06/09/2009 28* mostly clear, not photometric 0.9

! 06/10/2009 26* partially photometric 1.0


06/12/2009 26* photometric 0.8


07/08/2009 30 photometric 0.8

08/04/2009 30 photometric 0.9

08/27/2009 27 photometric 1.0


! 08/29/2009 29* not available n. a.

08/30/2009 28* photometric 0.9

09/07/2009 27 photometric 0.8




Table II—4: Overview of observation conditions at Calar Alto 2009 For a description of the Flag-row, see Table II—3. In nights where the usable images are marked with an asterisk (*), two sets of data were obtained —see Chapter 2.1.2 for details. For the night of April, 29 th analysis of the data is used with caution because no observation condition information were given.


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2.2.1 IRSF DATA

Data reduction of the IRSF datasets was completely automated using the SIRIUS Da-ta Reduction Pipeline developed by Nakajima, Y. (in-house). All reduction steps were performed by the service operator at SAAO. We had only access to the completely reduced images.

In the reduction process the following steps were made (for an in-depth description and motivation of the steps, see the explanations for the Calar Alto dataset):

Dark and flat field correction was applied to science and sky frames

Sky frames were generated and allocated to their respective science frames

Sky subtraction was done as specified

The dithered exposures were combined for every field

The resulting field of view and image dimensions are slightly higher than for a single frame, because of the dithering

2.2.2 CALAR ALTO DATA

The data reduction of the Calar Alto datasets was performed by the author, using maintenance frames provided by telescope service operators. All reduction steps are described thoroughly in the next paragraphs.

BIAS FRAMES

To enable a specific and normalized current/voltage level, CCDs are always set to an appropriate operating point, or bias. This manifests itself as a predetermined count level on every pixel on the CCD. To correct the science frames, this bias is deter-mined by making zero second exposures with closed shutter. Those exposures are taken at the beginning and end of every night and averaged.

In the reduction process, the bias frames were combined to a master-bias and then subtracted from every maintenance and science image using the IRAF routines no-

ao.imred.ccdred.zerocombine and noao.imred.ccdred.ccdproc.

DARK FRAMES

Although the detectors—forming the pixels every CCD is composed of—are made to react only to incoming photons (releasing electrons through the photoelectric ef-fect), the thermal emission of electrons has also to be taken into account. CCDs used in astronomy devices are cooled by liquid hydrogen to temperatures well be-low -100 °C, but nevertheless some thermal electrons still get emitted over time.

To correct for this effect which is proportional to the duration of an exposure, so-called dark frames are taken in regular intervals. As those dark frames are not al-

Section 2.2 – Data Reduction

13

ways available in the exact exposure times, generic darks are created by normalizing the exposure times to one second and later on scaling them accordingly. The scaled darks are then subtracted from all remaining maintenance frames and science imag-es. The used IRAF routines were noao.imred.ccdred.darkcombine and no-ao.imred.ccdred.ccdproc.

FLAT FRAMES

There are two main factors, which require flat frames: inherent differences in sensi-tivity of every CCD-pixel, and shadowing through dust grains and other particles in the optical path. To minimize those effects, the CCD is uniformly (“flat”) illuminated and different sensitivities are noted for every pixel. The uniform illumination can be achieved either by observing a part of the sky that is lit by the rising or setting sun (dawn/dusk flats, or sky flats), or by pointing the telescope to a screen in the dome (dome flats).

The resulting image is then normalized to one and the science frames are divided by the flat—thus boosting values from insensitive pixels and lowering values from high-sensitive pixels. Usually sky flats are preferred, because they more closely mimic conditions during the observations. Analysis of this dataset, though, revealed artifacts in the sky flat reduced images, thus the dome flats were used for correcting the science frames. The IRAF routines noao.imred.ccdred.flatcombine and

noao.imred.ccdred.ccdproc were used.

SKY FRAMES

As mentioned in Chapter 2.1.1, when observing at infrared wavelengths, additional emission of the atmosphere has to be filtered from the images. This is usually done by taking dedicated images of regions in the sky which show no extended emission (like nebulae, etc.). As our observations were performed as “filler”—i.e. whenever there was half an hour observing time left in the night—time constraints prevented the scheduling of additional sky frame observations.

In the planning stage of the proposal, it was believed that it would be possible to create artificial sky frames. Artificial sky frames can be produced by adding up the dithered images without shifting them. If a maximum rejection algorithm is used, point source emission is removed. Unfortunately, the extended emission from the H II region made it impossible to create images that only contained atmospheric emission. The sky “corrected” science frames showed many extended regions with negative counts. This clearly indicated that the subtracted flux was not contained in the original image (Figure II—6 illustrates this error).

The atmospheric emission is a large-scale phenomenon and should alter the star-light in the same way as its immediate surroundings. Careful comparison of magni-tudes derived from those sky-emission contaminated images with magnitudes taken


14

from the literature (2MASS10) revealed that the IRAF routines to compute the sur-rounding sky values are good enough to remove atmospheric emission on a per-star basis. Therefore the uncorrected images were used in the further analysis.

SCIENCE FRAMES

The dithered frames taken in one night, were each bias-, dark-, and flat-corrected and then registered and combined. The reduction was performed using the IRAF task noao.imred.ccdred.ccdproc. A mathematical representation is given in the

equation below:

The image registration was done completely unattended by a self-written script. The script made use of the cl.images.imcoords.starfind task to automatically de-

tect stellar objects on the images; the shifts were then computed with the task cl.images.immatch.xyxymatch using the sophisticated triangles algorithm,

which requires no initial guess; the geometric transformation matrix is computed by cl.images.immatch.geomap; and the final registration is done with

cl.images.immatch.gregister. The shifted images are finally combined with the IRAF routine cl.images.immatch.imcombine.

10 Two Micron All-Sky Survey (Skrutskie, et al., 2006)

Figure II—6: Erroneous flux subtraction on CAHA image

15

Chapter III – DATA ANALYSIS

This chapter describes techniques and programs used in analyzing the datasets and creating the lightcurves.

To accurately measure variability on the scale of a tenth or even hundredth of a magnitude is difficult, because even a constant signal is subject to varying observing conditions. Doing absolute photometry using a modeled point spread function (PSF), derived from isolated stars on the image, is the best photometry method for one single image. Widely used algorithms are e.g. DoPHOT (Schechter, et al., 1993) and DAOPHOT (Stetson, 1987). But even then, it is highly critical to subtract the correct background from the PSF—this is especially difficult in crowded and/or nebulous regions (as shown in (Schechter, et al., 1993)).

When dealing with a time series of images, other factors add to the difficulty: Vary-ing seeing conditions and air masses affect the sharpness of the PSF, and different angles and positions on the frame might alter—e.g. elongate or rotate—the shape of the PSF. It is also very difficult—or near impossible—to calibrate the derived magni-tudes from the different images accurately enough to detect variations on such a small scale.

There are a few methods available to deal with this problem. One method (Broeg, et al., 2005) is to create an artificial standard star in the frame from those stars which vary the least over a time series. We tested this method, but found the implementa-tion insufficient for our data. Another method that was finally chosen is image sub-traction, which only deals with the residuals after subtracting one master image from all other frames. We decided to use the algorithms developed by Alard & Lupton: ISIS.

Chapter III – Data Analysis

16

3.1 IMAGE SUBTRACTION : ISIS

ISIS uses image subtraction techniques to create frames which only show the resid-uals of variable objects. The idea behind this method is that it is easier to look at the differences between two frames than to calibrate each individual frame accurately. Another benefit of this method is the ability to easily identify “new” objects, like outburst from formerly too faint stars, and moving objects—although the latter is not relevant to the objective of this thesis.

To achieve the same image quality on every frame, different seeing conditions have to be exactly matched. Of course, there are two possibilities for the reference frame; either choose the image with the best seeing or with the worst seeing. The ad-vantage of choosing the “worst” image is that all other images are easily degraded to the blurry reference frame, but a lot of valuable information gets lost in the process (e.g. loss of sharpness, merging of close binaries, lower signal to noise ratio). ISIS chooses the image with the best seeing as the reference frame and later convolves the kernel solution with the reference frame to match its seeing to the individual seeing of each other frame.

3.1.1 ISIS OPERATION OVERVIEW

As a first step the images are registered, using an astrometric transformation to match the coordinate system of the reference image and all other images. This trans-formation is done by fitting a two-dimensional polynomial using 500 stars on every frame and then resampling each other frame on the grid defined by the reference frame. This interpolation is done using bicubic splines, resulting in excellent accura-cy and flux preservation.

To match the seeing on frames with quite different PSFs the algorithm makes use of the justified assumption that the majority of stars on any given image fluctuate in amplitude at most by one or two percent—meaning that most of the pixels of two images should be the same, if the seeing were identical. This is a classical least-square problem and in such terms is described as finding a kernel that will minimize the following sum, where R is the reference frame, K the convolution kernel and I the image to be aligned:

∑([ ]( ) ( ))

The convolution Kernel itself is variable over the entire image, to compensate for geometric distortion of the point spread function. To assure flux conservation even in the case of a varying Kernel, the sum of the Kernel function has to be constant at all points of the image. To construct such a Kernel function, a series of basis vectors is chosen that have zero sums (except for the first vector). This leads to the follow-ing Kernel formula:

Section 3.1 – Image Subtraction: ISIS

17

( ) ( ) ∑ ( ) ( )

With ; and ( ) ∑

a polynomial function. For details

on the mathematical concepts, see (Alard, 2000).

The convoluted images are then subtracted from a reference image constructed from the five sharpest frames (i.e. those with the smallest M FWHM). The subtracted images are square-added to provide a detection frame on which only the residuals of variable stars are visible. In the next step the positions are detected, either manually or by using an automatic detection threshold. The remaining positive or negative flux values are measured individually on each subtracted frame and are written to a table in combination with the Julian date of the observation.

The time series of flux variations is analyzed using an implementation of the Schwarzenberg-Czerny period search in uneven sampled observations (Schwarzenberg-Czerny, 1996). This algorithm uses periodic orthogonal polynomi-als to fit the observations and the analysis of variance (ANOVA) statistic to evaluate the quality of the fit. According to Schwarzenberg-Czerny, the orthogonal polynomi-als constitute a flexible and numerically efficient model of the observations. ANOVA statistics are known to have optimum detection properties as the uniformly most powerful test. The recurrence algorithm for expansion of the observations into the orthogonal polynomials is fast and numerically stable. The expansion is equivalent to an expansion into Fourier series. The resulting period (in hours), a confidence value, and a table containing phase and corresponding flux values are written.

3.1.2 ISIS ANALYSIS STEPS

This section lists the individual analysis steps performed in ISIS, together with all relevant parameters. ISIS consists of a series of shell scripts which perform the indi-vidual analysis steps. The names of the scripts and their function are summarized in Figure III—1.

PREPARATION OF THE IMAGES

Before ISIS can be run, the images need to be modified. Unfortunately, ISIS has very strict limitations on working fits-file sets. There are two main problems that need to be corrected first.

First, the images used in this analysis are created by combining several dithered frames, with short exposure times each. As the dithering is not always one hundred percent identical, each resulting image has slightly different resolutions, i.e., the x × y pixel count varies. In addition, even though all images are “reduced” (see Chap-ter 2.2), still some artifacts remain at the edges. To take care of this, all images are trimmed to the same dimensions.


18

Second, some of the raw frames contain very high counts—especially the Calar Alto images. This is because the stacked images were created by adding the individual frames, instead of averaging. This resulted in—worst case example—30 × 64,000 = 2 Million counts on one pixel in the image. As ISIS cannot handle such high pixel counts (the convoluted images are always blank), all images are normalized by con-verting them to a 16bit number format (65,536 distinct values per pixel). This cre-ates no alteration of the flux information, because the mathematical grade of accuracy is still higher than the physical accuracy. As ISIS adjusts all frames to the same overall level, no additional bias is created.

DETERMINING THE REFERENCE IMAGES

ISIS needs one image as reference for astrometry and a couple of images to build a reference frame for the image subtraction. These frames are automatically selected by a self-written analysis script using IRAF’s imcoords.starfind for the star de-

tection, and tv.imexamine for the FWHM measurements. In this step all observa-

tion times are converted to Julian dates and put in a list file for ISIS. The image with the best—i.e. sharpest—full-width at half-maximum is used as astrometric reference and the five best images are used for the photometric reference frame.

IMAGE REGISTRATION AND INTERPOLATION

The first script to be run after the preparations is interp.csh. This script registers

all images to the coordinate grid of the reference frame. The only relevant parame-ters are the degree of the polynomial used for the astrometric transformation be-tween the frames, and the detection threshold in counts per pixel for exclusion of cosmic ray impacts in the star detection.

Figure III—1: Flow chart displaying ISIS analysis steps

interp • Image registration & interpolation

ref • Build composite reference frame

subtract • Subtract reference frame from images

detect • Create weighted stack of subtracted images

find • Find variables in weighted stack image

phot • Do photometry on all subtracted images

czerny • Compute period and phase for lightcurves


19

BUILDING THE COMPOSITE REFERENCE FRAME

After the images are registered, a reference frame is constructed from the previously determined best images. The reason to use a combined image of the five best frames—instead of the best image—as reference frame, is to compensate for defects and artifacts on the individual frames (e.g. contamination by cosmic ray impacts on the detector). Although in this case each frame already is a combined frame, the ef-fects should not be prominent, but the program works also on non-combined imag-es.

To adjust for varying background intensity and the slightly different FWHM, all im-ages are transformed to the background level and seeing of the astrometric refer-ence frame and then combined using a three-sigma rejection from the median. This is done with the use of the script ref.csh. Important parameters in this step are the degree of the polynomial used to fit the differential background variations, the satu-ration limit of the detector, and the number of sub-divisions along both axes.

The nebulous background of M 17 made a subdivision of the images futile, because the significant change in the mean background level between the pieces would affect the photometry of stars near the edges of the tiles (see Figure III—2). In addition, the background fitting algorithm does not work well with the extended background emission of the nebula. Designed to compensate a smooth pattern in the background variation, e.g. vignetting, the algorithm erroneously tries to fit the spatially variable nebula. Therefore the degree has been set to zero—effectively disabling the back-

Parameter Value Description DEGREE 2 Degree of the polynomial astrometric transform COSMIC_THRESH 50,000 Threshold for cosmics detection

Table III—1: Parameters for ISIS interp.csh script

Figure III—2: Erroneous background computation using tiles on CAHA image


20

ground scaling. All existing vignetting patterns should have already been removed by the flat-field-correction.

SUBTRACTING THE REFERENCE FRAME FROM THE IMAGES

In the next step the subtract.csh script is used to subtract the reference frame,

created in the last step, from all images. Before the actual image subtraction takes place, the reference frame is convoluted with the current spatially variable kernel solution to match the image conditions—seeing, intensity, etc.—as good as possible.

A number of important parameters are used in this step: the characteristics of the kernel, the number of stamps for the computation of the background- and kernel-variations, and the degrees of the used polynomials. The Kernel characteristics in-clude the size of the convolution kernel and the number, degree, and standard varia-tion of the Gaussians to modulate the kernel. The number of stamps represents the granularity of the computation of the varying components in the background and kernel. A stamp is a quadratic sub-region of the image centered on a bright star. This star is then used to model the kernel for the surrounding regions of the image. The background characteristics of the stamps are used to fit the background polynomial. However, after several tests, this fitting is not used in this work. Enhancing the de-gree for the kernel spatial variations over a simple x/y-dependency considerably lengthened computing times without providing better subtracted images.

Parameter Value Description DEG_BG 0 Degree to fit differential background variations SATURATION 50,000 Saturation limit SUB_X 1 Number of sub-divisions of the image along x-axis SUB_Y 1 Number of sub-divisions of the image along y-axis

Table III—2: Parameters for ISIS ref.csh script

Parameter Value Description NSTAMPS_X 10 Number of stamps along X axis NSTAMPS_Y 10 Number of stamps along Y axis HALF_STAMP_SIZE 15 Half stamp size SUB_X 1 Number of sub-divisions of the image along x-axis SUB_Y 1 Number of sub-divisions of the image along y-axis DEG_SPATIAL 1 Degree to fit spatial variations of the kernel DEG_BG 0 Degree to fit differential background variations SATURATION 50,000 Saturation limit HALF_MESH_SIZE 9 FWHM of the modulated kernel NGAUSS 3 Number of Gaussians used in kernel modulation DEG_GAUSS1 6 Degree associated with first Gaussian DEG_GAUSS2 4 Degree associated with second Gaussian DEG_GAUSS3 3 Degree associated with third Gaussian SIGMA_GAUSS1 0.7 Standard deviation of first Gaussian SIGMA_GAUSS2 2.0 Standard deviation of second Gaussian SIGMA_GAUSS3 4.0 Standard deviation of third Gaussian

Table III—3: Parameters for ISIS subtract.csh script


21

CREATING A WEIGHTED STACK OF THE SUBTRACTED IMAGES

The—somewhat misnamed—next script detect.csh combines subtracted images in a way that enables easy detection of the variable objects in the dataset. The sub-tracted images, containing only the positive or negative residuals of variables, are square-added to a stacked or residual image. The resulting image (named var.fits) shows “stars” whose magnitude is proportional to the amount of varia-

bility they have (see Figure III—3). Non-variable stars are invisible on this image.

The only relevant parameters in this step are for controlling the removal of cosmics in building the stack-image, and the smoothing applied to the resulting image. To apply a cosmic rejection, one would normally use the median of a time series. This is not recommended, when dealing with certain kinds of variables, as—for example—one-time eruptions might be purged from the data. To distinguish cosmics from eruptive variables, the maximum in the time series is checked against the value of the nth deviations. If the nth deviation is less than half of the maximum, it is very like-ly that the absolute deviation is driven by a few points—i.e. a few defects or cosmics. Thus in this case the mean of the absolute deviation is clipped from the n largest deviations. Otherwise the mean of the absolute deviations is used.

Figure III—3: Comparison variability image to reference image Left: Variability image showing the stacked residuals of variable stars; right: same region from the reference image; 2 pairs of stars of comparable magnitude are marked on each frame—it is evident that the bottom right star is highly variable, while the top left star is completely non-variable; the comparison stars are slightly variable

Parameter Value Description N_REJECT 2 Nth deviation to check in cosmic rejection MESH_SMOOTH 3 Size of the smoothing mesh

Table III—4: Parameters for ISIS detect.csh script


22

MARKING OF VARIABLES IN THE WEIGHTED STACK IMAGE

Usually the next step is to automatically detect the variable objects in the residual image, using the script find.csh. This works reasonably well in regions of space without extended emission nebulae and will therefore be useable in the pipeline for IRIS. Unfortunately, in M 17 the background emission from the H II region makes the automatic detection useless, because the background of the stack image shows a high amount of noise (see Figure III—4).

For this thesis every residual image has been checked for variables per eye and the locations were manually marked. The list of variables was than matched against the automatically created lists. In this way, all detections were combined with the addi-tional data ISIS needs in further analysis steps. The only parameter used here is the detection threshold for variables. This could be set to a very low value to make sure every variable was in the automatic list. All faulty detections were later purged in the matching process.

Parameter Value Description SIG_THRESH 0.01 Threshold for detection of variables

Table III—5: Parameters for ISIS find.csh script

Figure III—4: Noise on the variability image Variability image showing the stacked residuals of variable stars along with consider-able background noise and artifacts from saturated stars and the nebula


23

DOING PHOTOMETRY ON ALL SUBTRACTED IMAGES

In the penultimate step in the ISIS analysis the actual photometry of all images takes place. ISIS uses a so-called point-spread function photometry method. In contrast to a simple aperture photometry—which measures the total flux inside a given aper-ture—PSF photometry tries to recreate mathematically the way the starlight is al-tered by the optics (hence the name: light from a point source is spread over an area).

The actual photometry is done by fitting a PSF model to a fixed position in the sub-tracted image. This PSF model is generated by convolving the current kernel solu-tion with a model of the PSF created in the reference image. Within each stamp-area in the reference image a PSF model is constructed by median stacking of bright stars. The PSF map for the reference is constructed by the program Bphot. The task

of using the PSF map and convolving it with the local kernel is done by Cphot. This

program also calculates the total flux by profile-fitting and writes the data to the lightcurve files. All these programs, as well as the image subtraction itself are man-aged by the shell script phot.csh.

The important parameters for the photometry are mostly dependent on the overall seeing conditions of the dataset—i.e. the full-width at half-maximum of the worst image. As the PSF model needs to be fitted to the broadest PSF, the radius must be large enough. The dimensions of the ring around each point-source in which the intensity of the background sky emission is measured are also defined by the PSF radius. In theory, the image could also be split in smaller parts, but—as discussed above—this was not done in this work. To avoid false flux values from the non-linear scale of the chip, the analogue-to-digital conversion factor (in essence: how many electrons are triggered by one incoming photon) and the saturation limit of the chip are given.

COMPUTING PERIOD AND PHASE OF THE LIGHTCURVES

The final step in the ISIS analysis is to look for periodic variability in the lightcurves and sort the values in a phase diagram. Before this, the differential flux values need to be converted to absolute flux. The images also need to be calibrated to further

Parameter Value Description RADPHOT 5.0 Radius for PSF magnitude fitting RAD_APER 7.0 Radius for PSF flux normalization RAD1_BG 15.0 Inner radius for the background calculation RAD2_BG 20.0 Outer radius for the background calculation SUB_X 1 Number of sub-divisions of the image along x-axis SUB_Y 1 Number of sub-divisions of the image along y-axis NB_ADU_EL 5 Conversion factor electrons per A/D-unit SATURATION 50,000 Saturation limit

Table III—6: Parameters for ISIS phot.csh script


24

convert the flux values to magnitudes. This is done using 2MASS stars as reference. The next two subchapters explain these steps in detail.

3.2 CALIBRATION: IRAF & 2MASS

The usual way to calibrate astronomical data is to include exposures of well-known standard stars during the observations. The “opportunity” nature of the observa-tions used in this thesis prevented the inclusion of standard star images. Unfortu-nately, there were also no infrared standard stars in the field of view.

As a solution to this dilemma, we used the 2MASS11 point source catalog to look for reference stars. Criteria for reference stars were, first, that they should be of medi-um brightness—i.e. not in the non-linearity realm of the detector. Second, that they—as a whole—cover a range of several magnitudes. Third, their photometric accuracy—according to 2MASS—should be very good (i.e. less than 0.05 mag). Fourth, they should be solitary and within an environment with negligible amount of background emission—i.e. no neighbor stars within reasonable aperture and no nebulous background. Fifth—and foremost to this study—, they should not be de-tected as being variable in any of the datasets.

Color-correction terms—normally needed to compensate for biases in slightly dif-ferent filter sets—could be neglected in this study, because the calibration was only used to bring all magnitudes to the same level. Even though color terms are used in certain diagrams throughout Chapter 4.3, they were only used to demonstrate tendencies and not for classification of single stars.

11 Two Micron All-Sky Survey (Skrutskie, et al., 2006)

RS-# Right

Ascension Declination

J mag

ΔJ mag

H mag

ΔH mag

K mag

ΔK mag

RS-1 18h 20m 59.59s -16d 04m 22.05s 12.80 0.02 11.62 0.02 11.15 0.02 RS-2 18h 20m 38.38s -16d 07m 32.41s 11.47 0.03 10.68 0.03 10.26 0.04 RS-3 18h 20m 16.16s -16d 05m 26.19s 12.45 0.03 12.08 0.02 11.93 0.03 RS-4 18h 20m 35.35s -16d 09m 17.01s 11.50 0.03 10.05 0.03 9.43 0.02 RS-5 18h 20m 45.45s -16d 09m 57.72s 12.12 0.04 10.31 0.04 9.49 0.03 RS-6 18h 20m 51.51s -16d 08m 47.72s 13.18 0.02 10.56 0.02 9.34 0.02 RS-7 18h 20m 40.40s -16d 12m 39.89s 11.46 0.02 10.89 0.03 10.77 0.03 RS-8 18h 20m 21.21s -16d 12m 06.85s 14.19 0.02 12.07 0.02 11.10 0.02 RS-9 18h 21m 03.03s -16d 11m 27.39s 13.54 0.04 10.86 0.03 9.53 0.02

Table III—7: 2MASS reference stars used in this study RS-# is the designation assigned to the reference stars; the coordinates are in J2000.0 Julian Equinox; J, H, and K refer to the Johnson-filter magnitudes derived from the 2MASS catalog; ΔJ, ΔH, and ΔK refer to the total photometric uncertainty given in the 2MASS catalog; the reference stars are marked in Figure III—5

Section 3.2 – Calibration: IRAF & 2MASS

25

For every set of images (1s, 10s, and 30s each in J, H, and K from IRSF, and the Calar Alto dataset) all nine reference stars were measured and compared to the literature values. For each set, a subset of reference stars, which generated the least average of the absolute deviations of data points from their mean value, was used to calculate the magnitude offset. The calibration accuracy was about one to two tenth of a mag-nitude, depending on the dataset. Given that no distinct calibration images were taken, this accuracy is reasonable, but later on made the combination of lightcurves difficult (see Chapter 4.2 for details).

Figure III—5: 2MASS reference stars used in this study RS-# is the designation assigned to the reference stars; for information on the stars see Table III—7


26

The photometry was done on the reference images created by ISIS in step 2 with the IRAF task noao.digiphot.daophot.phot. The critical parameters were chosen to

match those used in step 6 of the ISIS photometry (all phot-parameters are listed in

Table III—8). The reference flux measured by IRAF on the reference images could now be combined with the time dependent relative flux for all dates measured by ISIS. The resulting absolute flux values were converted to magnitudes for the lightcurve analysis.

3.3 LIGHTCURVE ANALYSIS

After all residual flux tables were converted to absolute flux, the periodic and phase analysis is carried out by the C implementation12 of the Schwarzenberg-Czerny method provided in the ISIS package. This algorithm calculates a variability period, on which most of the data points are in phase, and an error-value in percent, quanti-fying the fit of the phase to the actual data.

Three parameters can be passed to the czerny.exe: the minimum and maximum pe-riod in days, and the number of interfering periods. Although certain types of varia-bles may exhibit periodic behavior on different timescales, the resolution in time of the available data points is not sufficient to calculate distinct values of super posi-tioned periods. Therefore only one period is calculated. The three month (approxi-mately 100 days) cap for the longest period to fit is somewhat arbitrary, but

12 The programming language “C”

Parameter Value Description FWHMPSF 2.5 FWHM of the PSF in scale units SIGMA 6.0 Standard deviation of background in counts DATAMIN 0 Minimum good data value DATAMAX 50,000 Maximum good data value NOISE poisson Background noise model READNOISE 30 CCD readout noise in electrons EPADU 5 Gain in electrons per count CALGORITHM none Centering algorithm SALGORITHM median Sky fitting algorithm ANNULUS 15.0 Inner radius of sky annulus in scale units DANNULUS 5.0 Width of sky annulus in scale units APERTURES 5.0 Photometric aperture radius in scale units ZMAG 25 Zero point of magnitude scale

Table III—8: Parameters for IRAF noao.digiphot.daophot.phot task

Parameter Value Description A 0.2 Shortest period to fit (in days) B 100 Longest period to fit (in days) N 1 Number of interfering periods

Table III—9: Parameters for ISIS czerny routine

Section 3.3 – Lightcurve Analysis

27

computing requirements increase dramatically with the possible period range. The same is true for the ~5 hour cap for the shortest period.

The output of the czerny program consists of the phase and corresponding flux values, the period length, and the uncertainty of the fit. All values are saved in tables and then plotted using Gri13. An example plot is given in Figure III—6.

13 Gri is an extensible plotting language for producing scientific graphs, ©1991-2007 by Dan Kelley. Gri is distributed under the GPL 1.0 OpenSource license and available at http://gri.sourceforge.net/.

Figure III—6: Template Gri-plot For more lightcurves and detailed description see Chapter 4.2.2

http://gri.sourceforge.net/

29

Chapter IV – RESULTS & INTERPRETATION

This chapter contains discussions of the scientific results of this thesis. The first sec-tion lists different types of variable stars: low mass and intermediate mass young variables, and other classes of variables from more evolved stars. The second sec-tion lists statistics about the derived lightcurves and the deductions from those data plots. In the next section color-color diagrams and color-magnitude diagrams are used to show properties of the stellar population as a whole. The closing section compares the information gained in this work with the results of other observation campaigns on M 17.

4.1 TYPES OF YOUNG VARIABLES

The main objective for this thesis was to deal with young stellar objects and analyze their photometric variability. This section introduces different classes of infant stars and shows template lightcurves—both theoretical and actual data from this work. Although the primary interest concerns young stars, certain types of evolved stars also show variability. Those are—briefly—discussed in the end of Section 4.1.

4.1.1 LOW MASS YOUNG VARIABLES

Most stars are low mass stars, i.e. their mass as an evolved star is around 1 M⊙ or less. The formation of stars in this mass regime is well understood. Density fluctua-tions in the interstellar matter form gravitational centers of gas and dust, which—if the mass amounts high enough—become so-called “dense cloud cores”. The cloud core then contracts further to form a Class 0 object. Now, the protostellar object starts to grow by accreting mass from its envelope. At this stage the protostellar mass is much lower than the mass of the envelope.

Chapter IV – Results & Interpretation

30

Magnetic fields and shear velocities in the surrounding molecular cloud transfer angular momentum to the new forming star. The rotation flattens the envelope to an accretion disk. The state of equal mass of the protostar and of the accretion reser-voir—i.e. envelope and disk—marks the transformation to a Class I object. Most of the radiation of the protostar is absorbed and reemitted by the surrounding dust, but at this state the object becomes just visible in the near-infrared wavebands.

After the accretion on the protostar and the dissipation through magnetic fields and stellar winds of most of the envelope mass, the remaining mass is compressed to a flat disk by rotational forces. The accretion disk now only amounts to 1 percent of the mass of the protostar and the protostellar object becomes a TTauri star (Bally, et al., 2006).

CLASSICAL TTAURI (CLASS II)

First discovered and classified in 1945 by Alfred Joy (Joy, 1945) and the characteris-tics later refined by George Herbig (Herbig, 1962), TTauri stars are a well known phase in stellar evolution. Their main criteria for selection are the dominant Hα and Calcium emission lines, along with strong and variable X-ray emission.

Two main sources contribute to the brightness variability of TTauri stars: accretion from the surrounding disk material and strong magnetic fields. The infall of disk material onto the star is moving along small threads, causing hot spots at the place of impact. These hot spots typically cover very small areas—around one percent of the surface. Although in cases of higher accretion rates, larger spots are possible (Bertout, et al., 1996). The rotation of the star then leads to periodic variability in

Figure IV—1: V-band variability over time diagram of a typical Classical TTauri Photometric lightcurve illustrating light variations due to a changing mix of cold and hot spots on the surface of the Classical TTauri DF Tau (Ménard, et al., 1999)

Section 4.1 – Types of Young Variables

31

luminosity and color. The color variability is a result of the higher temperature (up to 1000 K difference) of the spots, which emit light at shorter wavelengths. The de-tected periods cover a range from days to multiple weeks, but they are not stable and can change over time (Bouvier, et al., 1993).

The contraction from widespread interstellar matter to a relatively compact stellar object has also compacted and amplified the magnetic fields therein. The cold spots around magnetic tubes also contribute to the lightcurve, although at a much lower level. Typical for Class II objects is an additional—irregular—variation in overall luminosity, further complicating period searches.

A typical lightcurve of a Classical TTauri is given in Figure IV—1.

WEAK-LINE TTAURI (CLASS III)

After the stellar envelope is completely depleted and the remaining circumstellar matter is coagulating to form protoplanetary bodies, accretion subsides and the re-maining variability is exclusively due to rotating cold magnetic spots. These giant star-spots can cover up to 40 percent of the stars surface and are at least 500 K cold-er than the remaining photosphere (see Figure IV—3 for illustration). The resulting periods are usually stable over decades, with small amplitude variations, reflecting

Figure IV—2: V-band magnitude vs. phase diagram of a typical Weak-line TTauri Lightcurve illustrating the constant phase of the Weak-line TTauri V410 over a time-scale of 15 years (Ménard, et al., 1999)


32

changes in the distribution and properties of the magnetic field.

The rotational periods commonly found in Weak-line TTauris is normally one week or less. Classical TTauris usually rotate at speeds resulting in periods of 6 to 14 days. Contracting rotating bodies should speed up to preserve the angular momentum, but the acceleration is slowed down by the interaction with the accretion disk still present in Class II sources (Bouvier, et al., 1993).

Both Classical TTauri and Weak-line TTauri stars occasionally undergo brief periods of flare-like activity, during which their brightness increases by a multiple. These outbursts are followed by a slow luminosity decline over several hours. Although no significant statistics on these events exist, it is believed, that these flares are a result of heightened magnetic activity on the surface (Bally, et al., 2006).

A phase-folded lightcurve of a typical Weak-line TTauri is given in Figure IV—2.

FU ORIONIS STARS (FUORS)

During the early phases of stellar evolution, when the protostar is still surrounded by an accretion disk, thermal instabilities in the disk or disruptions by a close com-panion can lead to an increase of the accretion rate by orders of magnitude (from

Figure IV—3: Doppler imaging illustrative of star-spots on a Weak-line TTauri Temperature maps of HD 26337 illustrating coverage and intensity of star-spots at four distinct phases (Strassmeier, et al., 1991); the star-spots are comparable to those found on Weak-line TTauri stars


33

∼ 10-7 M⊙ per year in the quiet TTauri phase to ∼ 10-4 M⊙ per year during the out-burst). TTauri stars experiencing this type of outbursts are called FU Orionis stars—or Fuors—, named after the star this phenomenon was first observed at (Herbig, 1977).

The light emitted by the disk outshines the star by factors of 100 to 1000—completely altering any color characteristics of the system and increasing the overall brightness by at least 4 magnitudes. The large luminosity increase occurs over sev-eral hundred days, while the slow decline towards “normal” brightness can take 50 or more years.

Figure IV—4 shows lightcurves from two of the best studied Fuor outbursts. Alt-hough the—few—known lightcurves are quite diverse, they all have in common the large increase in optical brightness and that they remain luminous for decades (Hartmann, et al., 1996). Statistics on known FU Orionis stars in combination with estimations on how much gaseous mass in a given volume of interstellar space is processed into new stars, lead to the assumption that a typical low-mass star ac-cretes about 10 percent of its mass during Fuor accretion phases.

Figure IV—4: Template lightcurves of FU Orionis outbursts The FU Ori photometry is taken from Herbig (1977), Kolotilov & Petrov (1985), and Kenyon, et al. (1988); the V1057Cyg photometric references are contained in Kenyon & Hartmann (1991); data compilation (see references therein) by Hartmann & Kenyon (Hartmann, et al., 1996).


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EX LUPI STARS (EXORS)

EX Lupi is the prototype of another class of eruptive TTauris. As a result of a massive infall of circumstellar material the accretion rate is heightened and the overall lumi-nosity increases up to 5 magnitudes. Similar to Fuor eruptions, a significant built-up of stellar mass is accomplished during outburst. These phases of higher accretion last for several months up to one and a half year—separated by quiescent periods over a few years, during which Exors resemble classical TTauri stars (Herbig, 2008).

During the outburst, most of the star’s additional luminosity is produced in an area close to the surface of the star, where the infalling material loses kinetic energy through atmospheric friction. As the hot-spot now dominates the light coming from the photosphere, the combined color becomes bluer. The hot-spot itself is cooler during outburst, albeit much larger (Lehmann, et al., 1995).

Even between outbursts Exors show an intrinsic variability of less than 25 percent of peak-to-peak flux difference. The model of a modestly flaring disk with a rounded inner ring is able to reproduce the spectral energy distribution. These disks with inner gaps are common in the transitioning phase between Class II and Class III ob-jects (Sipos, et al., 2009).

A compilation of all V-band photometry in the last 15 years is given in Figure IV—5.

Figure IV—5: V-band photometry of EX Lupi All validated V-band photometric measurements available for EX Lupi of the last 5000 days—covering a magnitude range of over 7 mag; the data was compiled using the lightcurve generator on the website of the American Association of Variable Star Ob-servers (http://aavso.org)

http://aavso.org/


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4.1.2 INTERMEDIATE MASS YOUNG VARIABLES

Star formation proceeds quite differently depending on mass. Low-mass star for-mation was covered in the previous section. For high-mass star formation (for stars more massive than 10 M⊙) the protostar is completely obscured during its pre main-sequence phase. In this section, types of intermediate-mass protostars are ex-plained.

HERBIG AE/BE STARS (HAEBE)

In 1960 George Herbig first christened the term HAeBe stars in a study of 26 young intermediate-mass stars. Further investigation of this type of stars resulted in three criteria which define the class. According to Waters & Waelkens (Waters, et al., 1998) they are of spectral type A or B with bright emission lines; they exhibit infra-red excess due to hot and/or cool circumstellar dust; and they are of luminosity class III to V (giants, sub-giants or dwarfs)14. The restricting second and third criteria exclude evolved Ae and Be stars—since their IR-excess is due to free-free emission from ionized gas—and post main-sequence B(e) supergiants. In addition to the in-frared excess, the circumstellar matter around HAeBe stars often causes intrinsic and variable polarization.

Besides the pronounced Algol-like minima (see below for a detailed description of the Uxor subclass) HAeBe stars show two types of photometric variability. First, a long-term fading or brightening may be related to after-effects of Fuor-like out-bursts or to changes in the amount of extinction on the line of sight. The second type of variability is an irregular flickering at low amplitude (smaller than 0.5 mag), pos-sibly due to activity in the stars photosphere or chromospheres, or an effect of stel-lar pulsation. Virtually all HAeBe stars are variable (Herbst, et al., 1999). See Figure IV—6 for a template lightcurve.

14 MK-Classification after W. W. Morgan and P. C. Keenan

Figure IV—6: Small-scale HAeBe variability Part of the lightcurve of AK Sco in the Strömgren photometric system—showing irreg-ular variations of about 0.5 mag over approximately 1000 days (Bibo, et al., 1991); the Strömgren y-filter is comparable to the Johnson V-filter.


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UX ORIONIS STARS (UXORS)

Some HAeBe stars exhibit large luminosity amplitude drops—so-called Algol-type minima. This subclass was designated “UX Orionis stars” (or “Uxors”) after its proto-type (Herbst, et al., 1994). Their V-band brightness suddenly drops for durations of several days to weeks by three to four magnitudes. Those darkened periods are sep-arated by much longer periods (in the range of years) of small brightness variations (indistinguishable from “normal” HAeBe star variability).

Typical for Uxors is their outstanding color variability. The observed color, gradually getting redder as the star fades (which is expected, if caused by dust), reverses at some point this trend and starts to become bluer. This phenomenon is called the “bluing effect” and is only observed when the Uxor fades into a deep brightness min-imum. It is explained in terms of the increasing relative contribution of blue scat-tered radiation when the star gets obscured by a dust cloud (de Wit, et al., 2002). This is supported by the increase of polarization during minima.

The source of the dust are either cometary clumps orbiting the star in a shell-like envelope (Grady, et al., 2000), or Uxors are HAeBe stars seen nearly edge-on—enabling clumps of gas and dust in the circumstellar disk to cross the line of sight due to hydrodynamic fluctuations in the puffed-up inner rim of the disk (Dullemond, et al., 2003). Herbst & Shevchenko (Herbst, et al., 1999) propose unsteady accre-tion—the same mechanism as in TTauri stars—as cause for the brightness varia-tions.

4.1.3 OTHER TYPES OF VARIABLES

Besides the aforementioned variable protostars, there are also many other types of variable stars—some are intrinsically variable, some are only variable to the ob-server due to external effects. Many of those are evolved stars, probably not found in

Figure IV—7: Lightcurve and color-variation of UX Orionis The left panel shows over 20 years of V-band observations of UX Orionis, displaying the quasi-periodic minima (~6 years) and an overall variability on smaller scales; the right panel shows the color variation during minima : first the light gets redder, than—after a turnaround at ~11 mag—it gets bluer again (Herbst, et al., 1999)

Section 4.2 – Statistics & Lightcurves

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a region as young as M 17, but they may be foreground or background objects. There might be some “contamination” of the data with these stellar classes, althoug the infrequence of most types makes it unlikely.

Other classes of intrinsically variable stars include pulsating variables (e.g., Cephe-ids, some types of Red Giant Stars, pulsating White Dwarfs), eruptive variables (e.g., Wolf-Rayet Stars, Flare Stars, Luminous Blue Variables), and cataclysmic variables (e.g., Supernovae, Novae, Dwarf Novae). Extrinsically variables include rotating stars (where the apparent luminosity is influenced by non-spherical form, star spots, or magnetic fields), stars with planetary transits, and eclipsing binaries.

Eclipsing binaries should be common in M 17, as binary (or multiple) star systems are very common in general. Depending on the inclination of the viewing angle, the proximity of the stars, and the spectral type (or mass) difference between primary and companion, the lightcurve may show a sinusoidal pattern or periods of constant brightness with recurring drops in intensity.

Although the question if multiple star systems already form as such or are gravita-tionally bound at an evolved stage is not entirely solved, there is ample evidence that protostars can form already as binaries. Recently (Wheelwright, et al., 2010), it was shown, that ¾ of all HAeBe stars in an unbiased sample were binaries. Further analysis confirmed that those systems were formed via disk fragmentation.

4.2 STATISTICS & LIGHTCURVES

This section presents statistics on the found variables: their reference magnitudes, the variability amplitude and period range, their spatial distribution, and compari-sons with all non-variable stars. It also showcases template lightcurves.

4.2.1 STATISTICS SUMMARY

The inspection for variable stars (see Section 3.1.2 for details) was done inde-pendently for both datasets—for the final analysis both lists were combined and the photometry was run again to make sure every information available was included in the data. Table IV—1 gives an overview of the number and percentage of detected variable stars per dataset.

Dataset Variable Stars Overall Stars Percentage of Variable Stars IRSF - J 377 1312 29% IRSF - H 446 2899 15% IRSF - K 454 4000 11% CAHA - K 740 7839 9%

Table IV—1: Overview of variable stars per dataset


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The ISIS routines assign amplitude and period calculations to every variable star (c.f. Section 3.3). The period computations are forced—i.e. even for non-periodic flicker-ing or a simple decline/rise in magnitude a period is calculated. A hint for these “ar-tificial” periods is given by the confidence value assigned to any fit. If the percentage of values following the period is low, it might be spurious.

But there are also physical reasons for a low confidence value—e.g., if an underlying period is blurred by low amplitude modulations the period value was detected cor-rectly and the confidence value would still be low. The influence of the low confi-dence period computations was tested in some scenarios (see “Period distribution” below), but as there was no bias evident the period statistics remain valid.

The field of view of the IRSF dataset covers only the inner half of the Calar Alto im-ages—albeit the core region around the exciting O stars (see Figure IV—8). The IRSF field will therefore be sometimes referred to as “M 17 core”, or “core region”. De-pending on the specific question, sometimes all three filters are used for analysis—limiting the area of interest to the core region—and sometimes the complete field of view is used—limiting the available filters to just the K filter. In the latter case, a

Figure IV—8: IRSF field of view superposed on CAHA field of view Background CAHA reference image; Inlet: RGB composite: IRSF J (B), H (G), and K (R)


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combination of detections and values from both the IRSF and CAHA dataset will be used.

The IRSF dataset does not include measurements of stars brighter than 9 mag, be-cause the flux values of those stars lie outside the linearity realm of the detector. This prevented accurate scaling by the ISIS routines which lead to remaining residu-als on the subtracted images—regardless of whether the star was variable or not. This was not evident in the CAHA data, in which even the bright stars were correctly fitted (up to 7 mag). After examination of the magnitude computation errors given by the IRAF photometry of the reference frames, all stars fainter then 16 mag for the IRSF frames and 14 mag for the CAHA data, respectively, where removed from the datasets.

STARS PER MAGNITUDE

Figure IV—9 reveals that the count of variable stars first steadily rises with decreas-ing magnitude, and then drops significantly. There are several factors contributing to this picture. One overall principle has to be kept in mind: the number of stars with lower mass is according to Green’s Initial Mass Function (IMF) much higher than the

Figure IV—9: Variable stars per magnitude – combined K Showing the combined count of detected variable stars in the K filter from both the CAHA and IRSF datasets; each red column represents the non-cumulative count of stars within a 1 mag interval, i.e. the “≤ 11 mag”-column only counts stars with magni-tudes > 10 mag and ≤ 11 mag. The bright yellow inserts denote the amount of stars with infrared excess; the red %-values at the top of the bars refer to the percentage of variable stars compared to all (including non-variable) stars; the yellow %-values at the side of the bars refer to the percentage of IRE-objects compared to variable stars


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number of more massive stars. This means that—translated to main sequence stars—there are more faint stars than bright stars.

But there is another class of bright objects: Pre-main-sequence stars, e.g. TTauris or HAeBe stars. Looking at the percentage of bright variables from all bright stars it is clearly evident, that there is an abundance of bright variables. 40 to 60 percent of stars brighter than 11 mag are variables. This is a definitive trend towards young stellar objects accounting for a huge part of the bright stars in M 17.

Going to fainter magnitudes of 13 mag and below, however, the count of variable stars drops somewhat faster than would be expected in this scenario. This is an arti-fact from the detection algorithm: small amplitude variations of stars with a weak signal to noise ratio are lost in the data. This is because a weighting factor depending on the reference flux level above the background emission prevents the classifica-tion as variable—if it were otherwise, nearly every faint star would be classified as variable, due to statistical noise fluctuations.

These drop in star count when going to fainter magnitudes is also visible in the per-centage of variable stars from all detected stars on the field. Although varying for different magnitude intervals, the quota of variable stars drops significantly for 13 mag and fainter. The last two bins (“≤ 15” and “≤ 16”) are only valid for the core region of M 17 and the overall star count was restricted to that area.

Another helpful clue to determine the class of variable star is the fraction of objects with infrared excess (c.f. Chapter 4.3.1 for a detailed description how the infor-mation about infrared excess was gained). Figure IV—9 clearly shows that the amount of variables with infrared excess (henceforth sometimes abbreviated “IRE”) rises steadily compared to the number of all variable stars. One likely explanation of this trend is that the fainter stars are still embedded in their protostellar envelopes. As dense dust shells not only obscure brighter sources, but also redden the light, it is certainly possible that those fainter objects are—at least in part—still deeply em-bedded classical TTauri stars.

This scenario is further supported by the fact that sources with infrared-excess show a trend towards being slower rotators than non-excess-sources (see Figure IV—10). This is in agreement with the concept of Weak-line TTauris rotating faster than Classical TTauris which are slowed down by their accretion disk and dust enve-lope.

The same trend is evident in the complete JHK dataset from the IRSF observations (Figure IV—11). Here a similar drop in overall count for stars with magnitudes fainter than 14 mag can be seen, as well as a sparseness of variable stars at varying cut-off points depending on the used filter.


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Figure IV—10: Rotational period comparison (incl. IR-excess stars) Showing the percentage of detected variable stars in the K filter from both the CAHA and IRSF datasets; the columns represent the fraction of stars showing variability periods in the given range (non-cumulative—q.v. Figure IV—9); red: sources without IR-excess; yellow: sources with IR-excess

Figure IV—11: Variable stars per magnitude – JHK (M 17 core) Showing results from the IRSF dataset; solid columns: detected variable stars in J, H, and K filters; transparent columns: overall count of detected stars in J, H, and K filters (non-cumulative—q.v. Figure IV—9); the rightmost column is a summary of all detect-ed (variable and non-variable) stars—the bars for the overall counts are truncated for ease of view, but the numbers are given in the table


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AVERAGE VARIABILITY

The average variability (the average of the absolute variability amplitude, not taking non-variable stars into account) for a given reference magnitude interval (Figure IV—12) slightly increases towards fainter magnitudes. There are two main reasons for this: one artificial reason, and one physical.

The artificial reason, which accounts especially for the steep rise at 14 mag and be-low was already mentioned and is due to the rejection of small-scale variability at sources with low signal to noise ratio. Faint stars are only classified as being varia-ble when their change in brightness is significant compared to noise level fluctua-tions. The result is of course, that the average magnitude goes up, as small brightness variations are removed from the sample.

HAeBe stars provide a physical reason for the same effect—and this one probably accounts for the slight increase in average variability visible even at the brighter magnitude intervals. Bright HAeBe stars are of early B spectral type, and those are found to be low amplitude variables (Herbst, et al., 1999). The reason for increasing amplitude for later (and hence fainter) spectral types is probably the growing con-vection in these protostars (Finkenzeller, et al., 1984).

Figure IV—12: Average variability depending on reference magnitude Showing results from the IRSF dataset to compare the different filters JHK, and the combined K data; the columns represent the number of stars showing variability am-plitudes in the given range (non-cumulative—q.v. Figure IV—9); the IRSF dataset contains no stars brighter than 9 mag


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VARIABILITY DISTRIBUTION

Moving from average variability to the actual magnitude of brightness variations in the sample (see Figure IV—13), two main facts are immediately evident: most stars detected as variable show only low-amplitude variations, and the percentage is the same for each filter and scope (the “combined K“ sample covers both a larger area of M 17 and a larger timeframe).

Half of the variable stars (48%) exhibit only brightness changes lower than one tenth of a magnitude. Another third (31%) varies up to a quarter of a magnitude and only eight percent show amplitude variations of more than 0.5 mag. This is expected and consistent with findings of other studies (see for example (Herbst, et al., 1999) or (Carpenter, et al., 2001)). The most extreme cases exhibit magnitude changes of over 3 mag.

The number of stars showing variability less than 0.05 mag is a bit lower compared to the next two bins. The reason for this is again twofold: Going to very low varia-tions makes it harder to detect variability at all. On the other hand most mechanisms responsible for protostar variability are more powerful—usually producing ampli-tude variations of at least 0.1 mag in the optical (less at longer wavelengths, but the principle is the same).

Even though the majority of stars exhibit only small-scale variability, this does not mean that these are not interesting objects. On the contrary—even if eruptive varia-bles are more exotic and seem therefore more thrilling, our understanding of the star formation process in general benefits from the greater statistics gained.

Taken, for example, Classical TTauri and Fuors: The star gains about ten percent of its mass during phases of high-accretion rate (i.e., Fuor outbursts), but these phases only last for a very small fraction of its lifetime as a protostar. That means that the probability to find a TTauri in this heightened state is much less—in an unbiased survey (the overall probability to detect something that is suddenly very bright is of course higher …). So when wanting to gain insights on star formation rates, frequen-cy, and efficiency information on all protostars is vital. This especially includes those protostars in more quiescent phases, since they form the biggest share.

PERIOD DISTRIBUTION

When looking at the period distribution (Figure IV—14), a similar picture emerges. Most of the periods ISIS calculates for recurring variability patterns are short—meaning that depending on filter and scope about half of the proposed periods are less than a day. When going to longer periods, the count of stars seems to decline with every bin, except for the “one to three days”-interval.


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Figure IV—13: Distribution of variability amplitudes Showing results from the IRSF dataset to compare the different filters JHK, and the combined K data; the columns represent the number of stars showing variability a m-plitudes in the given range (non-cumulative—q.v. Figure IV—9); the percentage val-ues denote the fraction of all variable stars in this variability range —the percentage values for each filter were nearly identical; to avoid clutter, only the average percent-age over all four datasets is printed

Figure IV—14: Distribution of variability periods Showing results from the IRSF dataset to compare the different filters JHK, and the combined K data; the columns represent the fraction of stars showing variability per i-ods in the given range (non-cumulative—q.v. Figure IV—9); see text for details


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A closer look at the individual data reveals a preferred period at about 24-26 hours. In the CAHA observations this “sweet spot” is extended to 22-26 hours. About 20% (IRSF dataset) to over 25% (CAHA dataset) of the stars show periodic patterns with this reoccurrence interval.

One has to be careful not to interpret too much into these figures, because many of the periods are in only 50% agreement with all measurements—i.e., only half of the time series data points follow the detected pattern. If the valid periods are restricted to those with more than two thirds of data points in agreement, the distribution flat-tens a bit, but remains similar. In fact, the abundance of “one day”-periods even in-tensifies.

To test whether this is a result of the observation pattern, the time between every observation was checked for distinctive features (Figure IV—15). Given that most observations were carried out when the target was near zenith position (to mini-mize airmass influence), many are about one day (or a multiple thereof) apart. But most observations are offset by less than 24 hours, while the preferred periods are slightly more than 24 hours.

The aspect that the confidence values are higher on many of these “one day”-periods leans more to the assumption, that there is in fact a physical explanation behind this. One possible scenario involves the variability behavior of Weak-line TTauris. Weak-line TTauris usually have periods near one day and shorter (Herbst, et al., 1994). In addition to this, they exhibit no flickering, which makes their lightcurves quite stable and would explain the good confidence values ISIS calculates for the derived phase patterns.

Figure IV—15: Time between observations The two bottom panels show the intermediate days between each observation (timeframes not shown (8 to 18 days) are empty); the two top panels zoom into an hour level (disentangling the first two overlapping data point groups) : the red dia-monds denote actual timeframes, the black diamonds denote remaining hours after subtracting multiples of whole days from longer (> 1 day) observation pauses


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COMPARISON CORE REGION TO OUTER REGIONS

The star formation in M 17 is probably driven in part by the winds of the exciting O stars. This might lead to distinct characteristics between variable stars in the inner core of M 17 and the outer regions. In this work the core region of M 17 is defined by a circle with four arcminutes radius around the center of the triangle spanned by the three most luminous O stars commonly known as CEN1, CEN2, and CEN3 (see (Chini, et al., 1980)). The locations of all variable stars can be seen in Figure IV—16.

The field of view of the JHK imaging at IRSF only covers the core region, so only re-sults from the CAHA observations are used in this comparison. Figure IV—17 shows the derived periods from both areas. Except for a slight tendency towards longer periods in the inner regions, the samples are nearly identical. The same is true for the comparison of the amplitude of the variations (Figure IV—18), except for low-amplitude variables. In the outer regions brightness variations smaller than 0.05 mag account for 30% of the variables stars, compared to only 12% in the core region.

Figure IV—16: Variables distribution for inner and outer M 17 The yellow circle marks the 240’’ radius around CEN1+2+3; variables on the inside are marked with turquoise diamonds; variables in the outer region are marked with purple crosses; the void region to the left is due to artifacts on the source frames


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Figure IV—17: Variability period comparison inner and outer M17 Showing data from the CAHA observations; the columns represent the fraction of stars with variability periods in the given range (non-cumulative—q.v. Figure IV—9). There is no dichotomy evident—see text for details

Figure IV—18: Variability amplitude comparison inner and outer M17 Showing data from the CAHA observations; the columns represent the fraction of stars with an amount of variability in the given range (non-cumulative—q.v. Figure IV—9). For an explanation of the deviation in the first bin confer to the text


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This seemingly significant difference is—unfortunately—to a very high extent com-pletely artificial. The core of M 17 is filled with luminous and spatially quickly vary-ing interstellar matter, gas filaments, and molecular clouds. This noisy background makes the detection of small scale variables extremely difficult in this area. The out-er regions are either dominated by dust in dark clouds or by areas with matter at much lower densities. The darker and more stable background eases the detection of low-amplitude variables and a factor of two to three in the detection rate is well explainable.

The variability studies give no indication to a different population of stars in the inner and outer parts of M 17. Recent research at even larger fields of view (Dörr, 2009) showed ongoing star formation in areas much farther away from the exciting O stars than those covered by the CAHA observations.

The current picture of the star forming region M 17 is much larger than thought be-fore. This fact is further backed up by the findings of this study.

4.2.2 TEMPLATE LIGHTCURVES

This section shows template lightcurves from both datasets, along with factual de-scriptions and interpretations. Table IV—2 (continued in Table IV—3 and Table IV—4) presents nine lightcurves representative for different types of young stellar objects. The lightcurve charts are designed to show in the upper panel the absolute magnitude variation over phase, with two consecutive phases printed (dots). The lower panel prints the actual magnitude over the observation time, given as modi-fied Julian date (filled diamonds).

Two figures (Figure IV—19 and Figure IV—20) are included to showcase examples of lightcurves, where the different timescales—the CAHA observations over a very long period, and the evenly spaced IRSF measurements with a fine temporal resolu-tion—prove especially helpful for interpretation of the lightcurves. One series of JHK lightcurves are presented for all filters as an example, how the amplitude of the var-iation is getting lower to longer wavelengths, while the periods stay the same (Figure IV—21).

The wavelength dependency of the variability amplitude is expected for TTauri stars with rotating hot or cool spots. The scenario that the amplitude gets lower at longer wavelengths can be reproduced by models containing one large hot spot, or a group of smaller hot spots. The opposite would be true for models containing cold spots (Bertout, et al., 1996).


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Star Dataset Filter Description Lightcurve Interpretation

lc027 CAHA K

A steady decline of 1.4 mag in the first epoch (2008), then a phase of stable brightness in the second epoch (2009)

Possible Exor candidate—similar to VY Tau

lc040 CAHA K

Very large period of ~500 days—0.6 mag amplitude—flickering

Possible HAeBe star

lc045 CAHA K

Variability of 1 mag over a very long period of ~200 days

Possible Exor candidate

Table IV—2: Template lightcurves


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lc110 IRSF K

Sinusoidal period over ~8 days of 0.1 mag—small flickering

Possible Classical TTauri

lc165 IRSF H

0.15 mag variability over a sharp period of 2.5 days—extended constant phases—no flickering

Possible Weak-line TTauri

lc190 CAHA K

Very large period of ~500 days—0.5 mag amplitude—flickering

Possible HAeBe star

Table IV—3: Template lightcurves (cont.)


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lc373 CAHA K

Constant decline or very large period (>2 years)—1 mag ampli-tude change—no flickering

Possible Fuor-eruption afteref-fects; Comparison to 2MASS magnitude (from 1999) of 12.6 mag supports the theory

lc527 CAHA K

A steady decline of at least 1 mag in the first epoch (2008), then a irregular variability of ~0.5 mag in the second epoch (2009)

Possible Exor candidate

lc637 IRSF K

4 day period with 0.3 mag varia-bility—plateau at minimum and maximum—no flickering

Possible Weak-line TTauri with rotating dark and hot spot

Table IV—4: Template lightcurves (cont.)


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Figure IV—19: Comparison of CAHA and IRSF lightcurves of star lc150 Left: Lightcurve of star lc150 at CAHA, covering 480 days, ~0.2 mag variability in K; right: same star at IRSF, covering 24 days, ~0.1 mag variability in K; the red bars de-note the same time interval in both graphs; the finer sampled IRSF lightcurve allows for a better period analysis, while the longer CAHA observations expand the three week snap-shot to a longer scale and reveal a doubled variability amplitude

Figure IV—20: Comparison of CAHA and IRSF lightcurve of star lc395 Left: Lightcurve of star lc395 at CAHA, covering 480 days, ~1 mag variability in K; right: same star at IRSF, covering 24 days, ~0.1 mag variability in K; the red bars de-note the same time interval in both graphs; only looking at the IRSF graph would lead to the conclusion that the star is getting brighter, albeit not by much —only when including the longer observation time at CAHA the whole picture emerges


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Figure IV—21: Comparison of lightcurves in three filters Lightcurves of star lc603: left J, middle H, right K; although the period stays the same in all three filters (7.7 days at 98% confidence) the amplitude of the variability changes by a factor of two from 0.5 mag in the filter at shortest wavelength ( J), over 0.35 mag in the H filter, to 0.25 mag at the longest wavelength (K)—see text for an explanation for this behavior


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Besides changes in time, one can also look for constant variability (which is no con-tradiction in itself, it just means that the pattern of the variability remains the same over a long timeframe compared to the derived period). Another star is used as an example for the stability of phase and amplitude over timeframe and wavelength—common to Weak-line TTauri (see for example the lightcurve for V410 Tau taken from the literature in Figure IV—2). This star (lc372, shown in Figure IV—22) is a K ≈ 10 mag star with a variability of 0.3 mag in all filters at a period of 8.2 days, which remains stable through all observations (IRSF and CAHA 2008 to 2009) at a 95% confidence interval.

This set of examples illustrates the wealth of information that can be gained from long time observing campaigns. It also demonstrates the need for observing periods in which images are taken at a fine temporal resolution. This provides a detailed grid of data to look for short periods common to TTauris and HAeBe stars in quiescent phases. The calculated periods can then be extrapolated and checked against the observations over longer timeframes.

Besides the opportunity to analyze the stability of the derived periods, the long timeframes make the detection of eruptive variables (Fuors, Exors, and Uxors) more probable. These one-time phenomenons can be easily missed in short observation campaigns, or even can be misinterpreted as recurring events, if the timeframes are coincidental.

Figure IV—22: Comparison of lightcurves derived from IRSF J, H, K, and CAHA K Example for steady variability patterns over all filters and timeframes —see text for details

Section 4.3 – Analysis-Diagrams over Time

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4.3 ANALYSIS-DIAGRAMS OVER TIME

Many properties of stars can be derived by observing in more than one filter, be-cause this allows the calculation of colors. Using the laws of Planck and Wien the temperatures of the stars can be estimated. This allows the creation of two types of diagrams: Color-color diagrams and color-magnitude diagrams. The latter is an equivalent to the famous Hertzsprung-Russel-Diagram. With the help of these dia-grams different populations or star types can be assigned to certain groups of stars.

The next section deals with color-color diagrams and Section 4.3.2 covers color-magnitude diagrams

4.3.1 COLOR-COLOR DIAGRAMS

Color-color diagrams (hereafter also referred to as two-color diagrams, or abbrevi-ated “TCDs”) are a potential tool to investigate both the properties of stellar photo-spheres and the circumstellar emission. As a first order approximation, stellar photospheres can be regarded as black-body emitters. Therefore, they can be uniquely described with one color, i.e. a combination of two filters. However, a se-cond color is diagnostic to determine any deviation from a photospheres’ energy distribution.

In addition to the observed colors for stars towards M17, unreddened stellar colors for main–sequence stars are displayed for comparison (taken from (Ducati, et al., 2001) in combination with (Aller, et al., 1982)). The locus of the main sequence is shifted with the distance modulus to correct for foreground extinction and distance. The most recent value (Hoffmeister, et al., 2008) of the distance towards M 17 of 2,100 parsec is used. Also the reddening vectors are displayed (from (Rieke, et al., 1985)) to visualize the “permitted” regions, in which stars are shifted due to inter-stellar reddening.

Stars with loci outside of the region spanned by the main sequence and the corre-sponding reddening vectors are either foreground or background giants (lying above and to the left of the upper reddening vector), or stars with additional circum-stellar dust emission (lying below and to the right of the lower reddening vector).

The adjacent area below the lower reddening vector is also called the “TTauri realm” (Meyer, et al., 1997) and the preferred loci of Classical TTauri stars due to line emis-sion and circumstellar dust emission, altering the infrared colors. Even further be-low those regions lies the area of HAeBe star colors. The theoretical loci of HAeBe stars depend highly on the used model for disk geometry and temperature. The plot-ted line here denotes the outer envelope of the permitted region (Lada, et al., 1992).


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Figure IV—23: Two color diagram – (J-H) vs. (H-K) Showing 426 stars from the IRSF dataset, for which in all three filters measurements could be made; 152 stars are marked in yellow, exhibiting IR -excess emission; 5 stars with (H-K) > 2.5 are omitted for legibility; the orange line denotes the locus of unre d-dened main sequence stars; the light red line denotes the Classical TTauri locus; the dark red line denotes the HAeBe Locus; the green arrows represent extinction vectors for AV=30 mag

Figure IV—24: Two color diagram – (J-H) vs. (H-K) with color variations Same data as before (q.v. Figure IV—23); in addition 8 stars with large color varia-tions are marked along with their pathways depending on observation time; see text for details


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Infrared two color diagrams are often used to differentiate main sequence stars simply reddened by foreground dust extinction from stars with additional infrared emission contributing to the starlight (e.g. from a warm dust cocoon or disk). The “(J-H) vs. (H-K)”-diagram (hereafter abbreviated “JHK-TCD”) is the most commonly used tool for this. TCDs involving even redder filters are preferable for this task, but observations at long wavelength filters (like Johnson L, or M) are much more diffi-cult (see for example (Scheyda, 2006)).

This technique is also used in this work to label stars as infrared excess emitters, but this definition depends strongly on possibly highly variable stars. To illustrate the danger of ambiguous classifications, pathways of some highly color variable stars are marked in Figure IV—24. Even if these color changes can be quite extreme for individual stars, the overall fraction of stars which fall into one or the other category remains remarkably stable. This fact allows continuing using the JHK-TCD as a tool to draw statistical conclusions. It is not, however, a good instrument to derive the properties of individual stars.

The classifications taken from Figure IV—23 are used in the subsequent color mag-nitude diagrams to mark infrared-excess stars. Of 426 stars which could be meas-ured in all three filters (abbreviated “JHK-source”), 152 stars are classified as IR-excess stars using the reference magnitudes. To test, whether this ratio changed over time, JHK-TCDs were created for all observation days of the IRSF dataset. For this purpose a subsample of stars was used, to keep the number of JHK-sources con-stant. The ratio of sources showing IR-excess varied between 30 and 36 percent (36 to 43 stars out of 119 stars).

It is safe to assume that the fraction of IR-excess sources of about one third of the whole sample remains stable throughout all observations. These statistics made the use of the subsample of stars determined in Figure IV—23 reasonable even for ob-servations from alternate dates.

4.3.2 COLOR-MAGNITUDE DIAGRAMS

Color-magnitude diagrams (abbreviated “CMDs”) allow—in principle—the determi-nation of spectral type and reddening of a star. Assuming main sequence stars with-out contaminating emission sources—e.g., warm dust shells—the star can be traced along the reddening vector. The point of intersection between reddening vector and zero age main sequence then determines the spectral type of the star.

Depending on the filters used, some ambiguities can arise. One main concern is the a priori unknown influence of excess emission. By using information gained from two-color diagrams it is possible to correct for excess emission—or at least to flag stars with IR-excess. Unfortunately, this only works for main sequence stars. Young stellar objects—especially low-mass stars—appear to be much brighter, before they complete their evolution to dwarfs and reach the main sequence.


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Figure IV—25: Color-magnitude diagram – J vs. (H-K) Showing 426 stars from the IRSF dataset, for which in all three filters measurements could be made; 152 stars from Figure IV—23 are marked in yellow, exhibiting IR-excess emission; the dark orange line denotes the loci of unreddened main sequence stars, with spectral type ranges indicated; the light orange line denotes the loci of pre-main sequence stars at the age of 10,000 years, with spectral type ranges indica t-ed; the red arrow represents the extinction vector of a B0 star for A V=30 mag

Figure IV—26: Color-magnitude diagram – K vs. (H-K) Showing 465 stars from the IRSF dataset, for which H and K measurements could be made; same notation as above (q.v. Figure IV—25)


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The loci of pre-main sequence stars in CMDs are called isochrones (from the Greek ‘iso’ meaning ‘equal’, and ‘chrono’ meaning ‘time’) and can be calculated for certain spectral types depending on the age. Particularly young isochrones can only be cal-culated for late-type stars, like K- and M-stars. The isochrones used in this work are taken from (Siess, et al., 2000)15.

The “J vs. (H-K)”-diagram (Figure IV—25) shows the characteristics of all stars for which simultaneous J, H, and K imaging exist. The zero age main sequence (shifted to correct for the distance towards M 17) in combination with the reddening path de-fines the area occupied by main sequence stars. Stars lying above the reddening vec-tor of a B0-type star are usually identified as O stars, but the overall Lyman continuum flux (Felli, et al., 1984) and recent studies (e.g., (Hoffmeister, et al., 2008)) do not allow that many O stars.

As said before, due to their large surface brightness young stars are also shifted into this region in the diagram. Looking at the area spanned by the 10,000 years iso-chrone and the reddening vector reveals that the loci of most variable stars are con-sistent with those of reddened YSOs. The information on infrared excess gained from the JHK-TCD (Figure IV—23) further support this, as many stars lying above the B0-reddening vector show IR-excess. This makes them ideal candidates for clas-sical TTauri-types or other YSOs still enclosed in their dust shells.

Unfortunately, the loci of main sequence and pre-main sequence stars is not well separated, as the inclination of the reddening vector is not so different from the ver-tical extend of the isochrones16. Even when taking into account that most O stars are not included in the sample (due to the saturation cut-off), a significant part of the stars above the B0-reddening vector might still be main sequence stars—they are variable stars, after all …

To get a broader angle between those lines, a “K vs. (H-K)”-diagram is useful. The downside is the dependence of both axes from another—in this case the K-filter measurements influence both. Figure IV—26 shows a bit more scatter than the “J vs. (H-K)”-diagram, but the area solely occupied by pre-main sequence stars wid-ens. The IR-excess information still helps disentangling the cloud of stars.

To test if the inclusion of shorter wavelengths—unimpaired by excess emission—helps to sort out evolved and young stars, optical magnitudes from other studies of M 17 were made use of. The best separation was gained using measurements in the B-filter (taken from (Schmidt, 2007)). The resulting “K vs. (B-K)”-diagram is shown in Figure IV—27. The inclusion of optical colors on the other hand meant to give up one of the best advantages of IR colors: deep penetration of dust clouds and very small obscuration by foreground extinction. This limited the sample to 55 stars for which photometric magnitudes existed in B and K—the two most separate filters available.

15 Web-calculator for isochrones: http://www-astro.ulb.ac.be/~siess/server/iso.html 16 Technically speaking, the zero age main sequence is also an isochrone …

http://www-astro.ulb.ac.be/~siess/server/iso.html


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Even an extreme color computation as (B-K) is no remedy for the ambiguities of age and spectral type. The disappointing fact that due to the limiting B magnitudes vir-tually no stars above the B0-vector remained can be ignored for the discussion. Four isochrones of increasing age are included in Figure IV—27. The inclusion of inde-pendent information about the reddening would enable the classification as pre-main sequence objects and even allow for an age determination in some cases. But due to the entanglement of differential reddening, infrared excess, and strong line emission this can only be achieved by spectroscopy.

Variability, infrared excess, and positions in the color-magnitude diagrams are very useful to determine possible youth, but only the combination of all indicators gives a reasonable enough certainty, which is still surpassed by results gained from spec-troscopy. Spectroscopy—on the other hand—is much more time consuming and only feasible for small numbers of stars. The results gained from simultaneous three-color survey over extended periods of time provide—though not always un-ambiguously—a good balance between benefit and cost (i.e., time).

Figure IV—27: Color-magnitude diagram – K vs. (B-K) Showing 55 stars for which B magnitudes (Schmidt, 2007) and K magnitudes (com-bined K dataset) existed; 7 stars from Figure IV—23 are marked in yellow, exhibiting IR-excess emission; the dark orange line denotes the loci of unreddened main s e-quence stars, with spectral type ranges indicated; the red lines denote (in decreasing brightness) the loci of pre-main sequence stars at the age of 10,000/100,000/ 300,000/1,000,000 years, with spectral type ranges indicated; the green arrow repr e-sents the extinction vector of a B0 star for AV=30 mag

Section 4.4 – Comparison to Other Studies

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4.4 COMPARISON TO OTHER STUDIES

Variability studies can be used as a tracer for star formation, or—to be precise—to find young stellar objects in different evolutionary stages. But there are additional means to detect protostars. This section briefly deals with other studies towards M 17 in different wavebands and using different characteristics as indicators for stellar youth. Subsections deal with infrared excess surveys, CO-band features in spectra, X-ray emission, and polarization studies.

Figure IV—28 shows a comparison of the detections. IR-excess and Polarization produce—statistically—the best agreement, with a conformity to about three quar-ters of the sources. Nearly half of the variable stars exhibit also X-ray emission. CO-bands seem to have the weakest correlation, but those data is gained from spectra—severely limiting the coverage. The most interesting aspect—and one of the best arguments for doing variability studies—is that the variability survey is in nearly one hundred percent agreement with a combination of all other studies.

To take a closer look on other detection methods which are susceptible for faint or bright objects, Figure IV—29 gives the percentage of matching sources per magni-tude interval. The IR-excess survey covers sources between 11 mag and 14 mag to 50% and replicates even fainter stars extremely good. The CO-band observations are only a good tracer for bright stars, which is expected, because spectra are not available for faint sources. The fractions of matching X-ray and polarization meas-urements are the most uniform over the whole magnitude range—the polarization results are in best agreement overall.

The next subsections give details on the compared studies.

Figure IV—28: Detection conformity comparison to other studies – overall Showing the conformity of IR-excess emission (yellow), CO-band features (orange), X-ray emission (red), and Polarization (brown) studies with the variability dataset (distinct samples to adept for different spatial coverage of individual studies) ; the rightmost column gives the fraction of variable stars in agreement with any combina-tion of the other criteria


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4.4.1 IR-EXCESS AND CO-FEATURES

This section presents results from a cross-check of in-house studies by Hoffmeister (J-, H-, and K-band photometry and K-band spectroscopy) and Scheyda (L-band pho-tometry, compilation of JHKL data) with the current dataset.

IR-EXCESS

Infrared excess surveys are used to find objects with remains of circumstellar mate-rial from the cloud core contraction phase. The presence of IR-excess separates sources younger than Class III17 from Class III and older objects. Wide-field imaging in different infrared filters is needed to cover a large area and calculate the colors. Preferred filters are J, H, K, and (to a lesser degree) L. (see Section 4.3 for uses of this technique on this dataset).

The IR-excess values taken from those studies seem to cover a much higher percent-age of variable stars, than those derived from the JHK-TCD of the IRSF dataset (q.v. Figure IV—23). This is due to the higher resolution, longer integration times and the use of a multitude of color-color diagrams—including those with L-band magni-tudes.

17 C.f. Section 4.1.1

Figure IV—29: Detection conformity comparison to other studies – per magnitude Same color code as in Figure IV—28, the numbers are an extrapolation to match the star count in the combined K dataset; the wide bright red columns denote the non-cumulative count of stars within a 1 mag interval (q.v. Figure IV—9)


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Figure IV—31 shows that the distribution of variable stars with IR-excess follows an elongated pattern around the western nebulosity rim. The highest densities arise between the brightest infrared sources CEN0 and CEN1.

The K-band measurements of Hoffmeister from 2000 allowed the addition of anoth-er epoch to the time series of photometry in this dataset. Given the different instru-ment, resolution and integration times, a calibration difference of 0.25 mag was still considered as a matching value. Figure IV—30 shows that one quarter (13% plus 12% for deviations larger than 0.25 mag and 0.5 mag respectively) of the variable stars exhibit brightness variations larger than the amplitudes in this dataset indi-cate. Limiting the sample to magnitude ranges brighter than 13 mag, this fraction was reduced to 6%/12%, hinting that the observation conditions (resolution, etc.) are more likely to produce differing values for faint stars. This percentage of stars with higher variability is expected and consists probably of objects with a constant rising/falling magnitude and/or eruptive variables.

Figure IV—30: Comparison of variability amplitude with archival data Showing the amount of variability larger than those derived from the magnitudes measured in the IRSF and CAHA dataset; K-band variable sources matched with ar-chival data from 2000; each column represents the non-cumulative count of stars within a 1 mag interval; the rightmost column is the sum over all magnitudes; colors: red denotes magnitude deviations > 0.5 mag, yellow denotes magnitude deviations between 0.5 mag and 0.25 mag, green denotes deviations smaller than 0.25 mag


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Figure IV—31: Distribution of variable stars showing IR -excess The contours denote areas of a constant star density of 1/3/6/10/14 stars per square-arcminute; derived from matching datasets; see text for detail

Figure IV—32: Distribution of variable stars showing CO-bands The contours denote areas of a constant star density of 0.5/1/2/3 stars per square -arcminute; derived from matching datasets; see text for detail


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CO-BANDS

YSOs can display CO band-heads in emission originating from warm and dense re-gions around the objects. This is most likely a result of the disk accretion process. Some low-luminosity Class II objects, i.e. Classical TTauri, also show strong CO ab-sorption bands—although this feature is usually more common in cool atmospheres of evolved stars. The spatial coverage of the CO spectra is focused towards the clus-ter center and the border area between in the southwest.

The distribution chart of the matching variables (Figure IV—32) shows also a con-centration of sources following the edge of the molecular cloud in the west. The highest densities are at a narrow peak in the north and a broader area in the south of the intersection H II region/molecular cloud. The CO-band observations were the only comparison study which was able to reproduce the detections in the dense mo-lecular cloud region.

4.4.2 X-RAY EMISSION

Young stellar objects in all evolutionary stages from Class I protostars to zero-age main-sequence stars show highly elevated levels of X-ray activity, exceeding the solar levels by several orders of magnitude. The majority of Weak-line TTauris, in-visible to Hα surveys, in many star-forming regions have been found through X-ray observations. The X-ray activity of non-accreting TTauri stars is consistent with that of rapidly rotating main sequence stars, the accreting stars show X-ray activity levels about 3 times lower. This may be related to changes of the coronal structure or the internal stellar structure induced by the accretion process (Preibisch, et al., 2005).

X-ray emission originates from free-free transitions of electrons in optical thin plasmas. Solar-type coronal loops are probably the dominant source of the observed X-ray emission. In TTauri stars, the origin of the X-ray activity is most likely a turbu-lent dynamo working in the stellar convection zone. (Preibisch, et al., 2005)

Chandra observations by Broos, et al. were used as X-ray point-source catalog (Broos, et al., 2007). The covered area can be seen in Figure IV—33. The distribution of variable stars exhibiting X-ray emission peaks between the brightest infrared sources CEN0 and CEN1. This is similar to the IR-sources distribution, but the densi-ty pattern is more circular.

4.4.3 POLARIZATION

Enhanced degrees of polarization are also a good tracer for circumstellar dust, as the starlight is polarized by passing through magnetically aligned dust grains. Zhibo et al. provided an unpublished catalog of a polarization study towards M 17—based upon observations made at the same facility in Sutherland, South Africa.


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Figure IV—33: Distribution of variable stars showing X-ray emission The contours denote areas of a constant star density of 1/3/6/9/12 stars per square -arcminute; derived from matching datasets; see text for detail

Figure IV—34: Distribution of variable stars showing polarization The contours denote areas of a constant star density of 2/4/8/13/18 stars per square-arcminute; derived from matching datasets; see text for detail


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Polarization as a tracer for embedded objects should reveal similar sources to IR-excess studies: sources younger than Class III, which are still surrounded by suffi-cient circumstellar matter. The distribution shown in Figure IV—34 is similar to the X-ray star density in position and extension, following a comparable circular pat-tern.

The comparison to other studies has shown that variability is a ubiquitous tool for the study of star formation. It comprises different populations of Young Stellar Ob-jects in one survey, including both faint and bright objects and covering dense areas otherwise only accessible through CO studies.

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Chapter V – SUMMARY & OUTLOOK

This chapter concludes this thesis with a brief summary of the results, an outlook on the use of variability studies, and a glimpse into the future of the IRIS project which will monitor star forming regions in the infrared for extended periods of time.

5.1 VARIABILITY AS A TRACER FOR STAR FORMATION

The results from this thesis show that variability surveys are an excellent tool to investigate ongoing star formation. The different types of young stellar objects all exhibit distinct features which were detectable in the lightcurves. The different timescales for the observations analyzed in this thesis proved that a well sampled interval is needed for accuracy of the derived periods. On the other hand, many characteristics of eruptive variables or long period variables are only traceable in long-term observations.

8000 stars were detected in a 16 square arcminutes field of view using the 3.5m telescope of the Calar Alto Observatory—4000 stars were detected in the smaller (8 arcmin²) IRSF field of view alone. This shows that even the use of small tele-scopes (1.4m mirror diameter) can lead to more than sufficient results. In the whole dataset about ten percent of variable stars could be identified. Variability amplitudes in the range from 0.01 mag to over 2 mag were found. The derived periods span timescales from hours up to months and—possibly—years. In addition to this, sim-ultaneous three-filter observations allowed the calculation of color-variability; ena-bling new insights (and constraints) on the common classification of stars only by using color-color diagrams.

The comparison with other detection methods—infrared excess, CO-band features, X-ray emission, and polarization—proved that variability surveys are able to deliver the most complete picture. Albeit each method is very successful on its own, some drawbacks still remain and can be overcome by variability studies. This may include

Chapter V – Summary & Outlook

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a bias towards certain types of stars, the dependency on time-consuming and diffi-cult spectroscopy, a limitation to bright objects, or the need for telescopes which are just not available for large surveys (e.g., satellites).

Variability studies can be performed with relatively small telescopes in a multitude of wavelengths—the infrared being the most versatile realm for the study of em-bedded sources, like protostars. One project aims to do this in the very near future:

5.2 THE IRIS PROJECT

The IRIS telescope (InfaRed Imaging Survey telescope) is a 31 inch alt-azimuth Na-smyth-system installed at the Observatorio Cerro Armazones in Chile. It is operated by the Astronomisches Institut der Ruhr-Universität Bochum18 in collaboration with the Institute of Astronomy of the University of Hawaii.

The telescope is equipped with a special infrared CCD detector operating in a con-tinuous read-out mode. The CCD has a resolution of 1024 × 1024 pixel²; covering a field of view of 13 × 13 square arcminutes. J, H, and K filters allow multi-color pho-tometry of target regions.

IRIS had successful “first light” on Thursday, the 6th of May 2010 and will deliver deep (limiting 10 sigma K magnitude: ~15.5 mag) infrared imaging of the southern sky. The allocated time for variability surveys of star forming regions allows for a wealth of data on YSOs in the near future.

The groundwork laid in this thesis will help scientists and students to use the data from IRIS to gain new insights on the formation of young stars.

18 Astronomical Institute of the Ruhr-University Bochum

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Chapter VI – ACKNOWLEDGEMENTS

First and foremost I want to thank my supervisor and “Doktorvater” Prof. Chini for the invaluable inputs to this work. He suggested the topic of this thesis and he al-ways thought of ways to get more information out of the data. His patience and ded-ication to always be there for his students (even when he was far away at our observatory in Chile, or when he rather had a thousand other things to do …) was greatly appreciated. I was inspired by his “Introduction to Astrophysics”-lecture to join the field of astronomy—ten years ago—and I did not regret it ever since. His support during my study and my diploma convinced me to write my thesis under his supervision.

I also want to thank my family—my father, mother and stepfather—for making it possible, that I could go to the university. Their moral (and financial) support was deeply appreciated.

Many thanks deserve my colleagues at the AIRUB who made working there a very pleasant experience. I could always bother my roommates Ina and Ramon with hun-dreds of questions—a favor they love to return … I also want to acknowledge the help of Vera for letting me use her datasets (including taming the “mother of all ta-bles”), providing me with valuable research tips, and pointing me to some very use-ful websites. I also want to thank our secretary—Martina—for becoming a good friend in the short time we worked (and talked and talked and …) together.

For help with all kinds of software my thanks go out to Tim Falkenbach & Roland Lemke (AIRUB), Stefanie Rätz & Thomas Eisenbeiß (Jena), and Petr Kabath & Thom-as Fruth (DLR). Especially Petr was a great help understanding ISIS.

Last—but certainly not least—my thanks belong to my beloved Catrin. Not only for being the wonderful woman she is and helping me through difficult times, but also for proofreading this thesis (again and again!) and making sure the language and grammar resemble something called English … You are great!

And a final “Thank you!” to all I forgot mentioning—it’s not that I don’t appreciate your help, I just have a terrible memory. I hope you don’t take it amiss …

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BIBLIOGRAPHY

Alard C. and Lupton R. H. A Method for Optimal Image Subtraction [Journal] // Astrophys. J.. - 1998. - Vol. 503. - p. 325+.

Alard C. Image subtraction using a space-varying kernel [Journal] // Astron. Astrophys. Suppl.. - 2000. - Vol. 144. - pp. 363-370.

Aller L. H. [et al.] Landolt-Börnstein: Numerical Data and Functional Relationships in Science and Technology - New Series '' Gruppe/Group 6 Astronomy and Astrophysics '' Volume 2 Schaifers/Voigt: Astronomy and Astrophysics / Astronomie und Astrophysik '' Stars and Star Clusters / Sterne und Sternhaufen [Book] / ed. Huckenholz, Czermak, V.,. - 1982.

Bally J. and Reipurth B. The Birth of Stars and Planets [Book]. - Cambridge : Cambridge University Press, 2006. - 978-0-521-80105-8.

Bertout C. [et al.] Photometric Observations of YY Orionis: New Insight Into the Accretion Process [Journal] // Astron. J.. - 1996. - Vol. 112. - p. 2159+.

Bibo E. A. and The P. S. The type of variability of Herbig Ae/Be stars [Journal] // Astron. Astrophys. Suppl.. - 1991. - Vol. 89. - pp. 319-334.

Bouvier J. [et al.] Coyotes-I - the Photometric Variability and Rotational Evolution of T-Tauri Stars [Journal] // Astron. Astrophys.. - 1993. - Vol. 272. - p. 176+.

Broeg C., Fernández M. and Neuhäuser R. A new algorithm for differential photometry: computing an optimum artificial comparison star [Journal] // Astronomische Nachrichten. - 2005. - Vol. 326. - pp. 134-142.

Broos P. S. [et al.] The Young Stellar Population in M17 Revealed by Chandra [Journal] // Astrophys. J. Suppl.. - 2007. - Vol. 169. - pp. 353-385.

Carpenter J. M., Hillenbrand L. A. and Skrutskie M. F. Near-Infrared Photometric Variability of Stars toward the Orion A Molecular Cloud [Journal] // Astron. J.. - 2001. - Vol. 121. - pp. 3160-3190.

Chini R. [et al.] The formation of a massive protostar through the disk accretion of gas [Journal] // Nature. - 2004. - Vol. 429. - pp. 155-157.

Chini R. and Hoffmeister V. Star Formation in M17 [Book Section] // Handbook of Star Forming Regions, Volume II / ed. B. Reipurth,. - 2008.

Chini R., Elsaesser H. and Neckel T. Multicolour UBVRI photometry of stars in M 17 [Journal] // Astron. Astrophys.. - 1980. - Vol. 91. - pp. 186-193.

ii

de Wit W. J., Beaulieu J. P. and Lamers H. J. G. L. M. A Search for HAeBe stars in the bar of the Large Magellanic Cloud based on optical variability [Journal] // Astron. Astrophys.. - 2002. - Vol. 395. - pp. 829-844.

Dörr M. Diploma Thesis [Report]. - Bochum : [s.n.], 2009.

Ducati J. R. [et al.] Intrinsic Colors of Stars in the Near-Infrared [Journal] // Astrophys. J.. - 2001. - Vol. 558. - pp. 309-322.

Dullemond C. P. [et al.] Explaining UX Orionis Star Variability with Self-shadowed Disks [Journal] // Astrophys. J. Lett.. - 2003. - Vol. 594. - pp. L47-L50.

Felli M., Churchwell E. and Massi M. A high-resolution study of M17 at 1.3, 2, 6, and 21 CM [Journal] // Astron. Astrophys.. - 1984. - Vol. 136. - pp. 53-64.

Finkenzeller U. and Mundt R. The Herbig Ae/Be stars associated with nebulosity [Journal] // Astron. Astrophys. Suppl.. - 1984. - Vol. 55. - pp. 109-141.

Fosbury R. A. E. [et al.] Massive Star Formation in a Gravitationally Lensed H II Galaxy at z = 3.357 [Journal] // Astrophys. J.. - 2003. - Vol. 596. - pp. 797-809.

Grady C. A. [et al.] Infalling Planetesimals in Pre-Main Stellar Systems [Journal] // Protostars and Planets IV. - 2000. - p. 613+.

Hartmann L. and Kenyon S. J. The FU Orionis Phenomenon [Journal] // Ann. Rev. Astron. Astrophys.. - 1996. - Vol. 34. - pp. 207-240.

Herbig G. H. Eruptive phenomena in early stellar evolution [Journal] // Astrophys. J.. - 1977. - Vol. 217. - pp. 693-715.

Herbig G. H. History and Spectroscopy of EXor Candidates [Journal] // Astron. J.. - 2008. - Vol. 135. - pp. 637-648.

Herbig G. H. The properties and problems of T Tauri stars and related objects. [Journal] // Adv. in Astron. and Astrophys.. - 1962. - Vol. 1. - pp. 47-103.

Herbst W. [et al.] Catalogue of UBVRI photometry of T Tauri stars and analysis of the causes of their variability [Journal] // Astron. J.. - 1994. - Vol. 108. - pp. 1906-1923.

Herbst W. [et al.] The rotation period and inclination angle of T Tauri [Journal] // Astrophys. J. Lett.. - 1986. - Vol. 310. - pp. L71-L75.

Herbst W. and Shevchenko V. S. A Photometric Catalog of Herbig AE/BE Stars and Discussion of the Nature and Cause of the Variations of UX Orionis Stars [Journal] // Astron. J.. - 1999. - Vol. 118. - pp. 1043-1060.

Hoffmeister V. H. [et al.] The Stellar Population of M17 [Journal] // Astrophys. J.. - 2008. - Vol. 686. - pp. 310-324.

iii

Joy A. H. T Tauri Variable Stars. [Journal] // Astrophys. J.. - 1945. - Vol. 102. - p. 168+.

Lada C. J. and Adams F. C. Interpreting infrared color-color diagrams - Circumstellar disks around low- and intermediate-mass young stellar objects [Journal] // Astrophys. J.. - 1992. - Vol. 393. - pp. 278-288.

Lehmann T., Reipurth B. and Brandner W. The outburst of the T Tauri star EX LUPI in 1994. [Journal] // Astron. Astrophys.. - 1995. - Vol. 300. - p. L9+.

Ménard F. and Bertout C. The Nature of Young solar-Type Stars [Conference] / ed. Lada C. J. and Kylafis N. D.. - 1999. - p. 341+.

Meyer M. R., Calvet N. and Hillenbrand L. A. Intrinsic Near-Infrared Excesses of T Tauri Stars: Understanding the Classical T Tauri Star Locus [Journal] // Astron. J.. - 1997. - Vol. 114. - pp. 288-300.

Preibisch T. [et al.] The Origin of T Tauri X-Ray Emission: New Insights from the Chandra Orion Ultradeep Project [Journal] // Astrophys. J. Suppl.. - 2005. - Vol. 160. - pp. 401-422.

Rieke G. H. and Lebofsky M. J. The interstellar extinction law from 1 to 13 microns [Journal] // Astrophys. J.. - 1985. - Vol. 288. - pp. 618-621.

Schechter P. L., Mateo M. and Saha A. DOPHOT, a CCD photometry program: Description and tests [Journal] // Publ. of the Astron. Soc. of the Pacific. - 1993. - Vol. 105. - pp. 1342-1353.

Scheyda C. M. Diploma Thesis [Report]. - Bochum : [s.n.], 2006.

Schmidt A. Diploma Thesis [Report]. - Bochum : [s.n.], 2007.

Schwarzenberg-Czerny A. Fast and Statistically Optimal Period Search in Uneven Sampled Observations [Journal] // Astrophys. J. Lett.. - 1996. - Vol. 460. - p. L107+.

Siess L., Dufour E. and Forestini M. An internet server for pre-main sequence tracks of low- and intermediate-mass stars [Journal] // Astron. Astrophys.. - 2000. - Vol. 358. - pp. 593-599.

Sipos N. [et al.] EX Lupi in quiescence [Journal] // Astron. Astrophys.. - 2009. - Vol. 507. - pp. 881-889.

Skrutskie M. F. [et al.] The Two Micron All Sky Survey (2MASS) [Journal] // \aj. - 2006. - Vol. 131. - pp. 1163-1183.

Stetson P. B. DAOPHOT - A computer program for crowded-field stellar photometry [Journal] // Publ. of the Astron. Soc. of the Pacific. - 1987. - Vol. 99. - pp. 191-222.

iv

Strassmeier K. G. [et al.] Doppler imaging of high-latitude SPOT activity on HD 26337 [Journal] // Astron. Astrophys.. - 1991. - Vol. 247. - pp. 130-147.

The P. S. The photometric behavior of Herbig Ae/Be stars and its interpretation [Conference] / ed. The P. S., Perey M. R. and van den Hoevel E. P. J.. - 1994. - Vol. 62. - p. 23+.

Tomaney A. B. and S. A. P. Expanding the Realm of Microlensing Surveys with Difference Image Photometry [Journal] // Astron. J.. - 1996. - Vol. 112. - p. 2872+.

Vrba F. J. [et al.] Further evidence for rotational modulation of the light from T Tauri stars [Journal] // Astrophys. J.. - 1986. - Vol. 306. - pp. 199-214.

Vrba F. J. [et al.] Photometric and spectroscopic monitoring of AA Tau, DN Tau, UX Tau A, T Tau, RY Tau, LK CA 4, and LK CA 7 [Journal] // Astron. J.. - 1993. - Vol. 106. - pp. 1608-1626.

Waters L. B. F. M. and Waelkens C. Herbig Ae/Be Stars [Journal] // Ann. Rev. Astron. Astrophys.. - 1998. - Vol. 36. - pp. 233-266.

Wheelwright H. E., Oudmaijer R. D. and Goodwin S. P. The mass ratio and formation mechanisms of Herbig Ae/Be star binary systems [Journal] // Monthly Notices Roy. Astron. Soc.. - 2010. - Vol. 401. - pp. 1199-1218.

Wilking B. A., Schwartz R. D. and Blackwell J. H. An H-alpha emission-line survey of the rho Ophiuchi dark cloud complex [Journal] // Astron. J.. - 1987. - Vol. 94. - pp. 106-110.

CURRICULUM VITAE

PERSONAL DATA

Name Claus-Michael Scheyda Date of Birth 07/22/1977 Place of Birth Bochum Nationality German

EDUCATION

SCHOOL

1984 – 1988: Gemeinschaftsgrundschule Borgholzstraße; Bochum

1988 – 1997: Graf-Engelbert-Schule, Städt. Gymnasium für Jungen u. Mädchen; Bochum

UNIVERSITY

1998 – 2005: Study of Physics and Astronomy at the Ruhr-Universität Bochum

2005 – 2006: Diploma Thesis “The Young Cluster in M 17 – Circumstellar Emis-sion at 3.8 µm” at the Ruhr-Universität Bochum

2006 – Present: Thesis “Variability of Young Stars” at the Ruhr-Universität Bochum

LEBENSLAUF

PERSÖNLICHE DATEN

Name Claus-Michael Scheyda Geburtsdatum 22.07.1977 Geburtsort Bochum Nationalität Deutscher

BILDUNGSWEG

SCHULE

1984 – 1988: Gemeinschaftsgrundschule Borgholzstraße; Bochum

1988 – 1997: Graf-Engelbert-Schule, Städt. Gymnasium für Jungen u. Mädchen; Bochum

UNIVERSITÄT

1998 – 2005: Physik und Astronomie Studium an der Ruhr-Universität Bochum

2005 – 2006: Diplomarbeit “The Young Cluster in M 17 – Circumstellar Emissi-on at 3.8 µm” an der Ruhr-Universität Bochum

2006 – Heute: Dissertation “Variability of Young Stars” an der Ruhr-Universität Bochum

Variability of Young Stars

Documents

Transcript of Variability of Young Stars