Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*,...

27
Irene Di Palma * , HeinzBernd Eggenstein * , David Keitel * , Maria Alessandra Papa *# , Sinéad Walsh # *Max Planck InsDtute for GravitaDonal Physics (Albert Einstein InsDtute) and Leibniz Universität Hannover # University of Wisconsin – Milwaukee LIGO Doc # G1500557

Transcript of Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*,...

Page 1: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

Irene  Di  Palma*,  Heinz-­‐Bernd  Eggenstein*,  David  Keitel*,  Maria  Alessandra  Papa*#,  

Sinéad  Walsh#  

*Max  Planck  InsDtute  for  GravitaDonal  Physics                  (Albert  Einstein  InsDtute)  and    Leibniz  Universität  Hannover  

#  University  of  Wisconsin  –  Milwaukee    LIGO  Doc  #  G1500557  

Page 2: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

 •  Brief  search  overview  •  The  deep  follow-­‐up      •  Outlook  

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   2  

Page 3: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Data  from  LIGO  L1  and  H1  detectors  

•  Spans  255  days  (with  gaps)  of  S6  

•  ~  1017  different  waveforms  searched  for.    Parameters  are:  –  Sky  posiDon:  all  sky    

 –  GW  emission  frequency:  

 –  First  order  spindown:        

•  CompuDng  Resource:  Einstein@Home    –  distributed  volunteer  compuDng  project    –  compuDng  power:  order  of  1Peta  FLOPS      

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   3  

�2.6⇥ 10�9 Hz/s < f < 3.1⇥ 10�10 Hz/s

50 Hz < f < 510 Hz

Page 4: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Detector  disturbances    •  Outliers  compeDng  with  “interesDng”  outliers  for  compuDng  Dme    

       

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   4  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Page 5: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Principle  #1:  Do  not  allow  outliers  from  detector  disturbances  to  limit  our  sensiDvity  

   

•  Principle  #2:  Study  the  recovery  of  signals  at  the  border  of  detectability  and  opDmize  the  search  accordingly  

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   5  

Page 6: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Preprocessing:  Replacing  known  lines  in  detector  data  with  syntheDc  Gaussian  Noise      

Before  

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   6  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Page 7: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Preprocessing:  Replacing  known  lines  in  detector  data  with  syntheDc  Gaussian  Noise  

Ajer:        

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   7  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Page 8: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Line  Robust  DetecDon  StaDsDc  [Keitel,Prix,Papa,Leaci,Siddiqi,  Phys.  Rev.  D  89,  064023  (2014)]    

•  Published  &  implemented  ajer  E@H  all-­‐sky  run  was  over  •  Re-­‐applied  to  returned  results  

Before:    

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   8  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Page 9: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Line  Robust  DetecDon  StaDsDc    [Keitel,Prix,Papa,Leaci,Siddiqi,  Phys.  Rev.  D  89,  064023  (2014)]    

•  Published  &  implemented  ajer  E@H  all-­‐sky  run  was  over  •  Re-­‐applied  to  returned  results  

Ajer:    

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   9  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Page 10: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Bulk  classificaDon  of  disturbed  regions  of  parameter  space:  by  visual  inspecDon  

•  Exclude  or  reserve  for  special  analysis    

 

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   10  

DetecDon

 staD

sDc  

f  

Cartoon,  not  actual  data  

Threshold  for    “deep”  follow-­‐up  

Page 11: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Even  weak  signals  more  likely  to  produce  clusters  of  candidates  than  Gaussian  noise  

•  è  use  clustering  to  find  such  clusters  (in  4-­‐D  parameter  space)  

 

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   11  

Page 12: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  è  Dealing  with  many  (  e.g.  10s  of  millions)  potenDally  weak  signal  candidates  

•  To  claim  detecDon:  increase  SNR  •  SoluDon:  Hierarchical  Searches  Successively  improve  SNR  and    narrow  down  search  volume:  –  finer  grids  (sky,  f,  fdot  ,fddot,…),  and/or  –  longer  Tcoh  and/or    –  longer  Tobs  (but:  sensiDvity  evoluDon    of  detectors,  long  gaps  between  science  runs.)        

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   12  

Page 13: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Given  a  set  of  candidates  from  stage  i  this  is  what  one  needs  to  figure  out  to  stage  i+1:  –  Set  CompuDng  budget  for  stage  i+1  – Determine  selecDon  criteria  for    “look-­‐further”  candidates  from  stage  i      

– Any  candidates  lej?  (else  done)  •  Determine  necessary  follow-­‐up  volume  around  candidate  nominal  parameters  

•  Determine  search  set-­‐up:    template  density,  method,  Tcoh,  Tobs,…    

   

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   13  

Determined  b

y  

previous  sta

ge  

Depends  on  search  volume  and  budget  per  

candidate  

Page 14: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   14  

2F  

•  CompuDng  Budget:  For  the  first  Dme,  Einstein@Home  used  also  for  follow  up  searches    

•  OpDmizing    search  setups:  – Mostly  driven  by    Monte  Carlo  experiments  •  On  a  set  of  weak  fake    test  signals  represenDng  

       candidates  at  the  edge  of    detectability  

•  On  noise  (we  take  real  data  as  representaDve  of  noise)    

Page 15: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  CompuDng  Budget:  For  the  first  Dme,  Einstein@Home  used  also  for  follow  up  searches    

•  OpDmizing    search  setups:  – Mostly  driven  by    Monte  Carlo  experiments  •  On  a  set  of  weak  fake    test  signals  represenDng  

       candidates  at  the  edge  of    detectability  

•  On  noise  (we  take  real  data  as  representaDve  of  noise)    

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   15  

2F  

Page 16: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   16  

E@H  all  sky  search    

E@H  Follow-­‐Up  Run  #1  

E@H  Follow-­‐Up  Run  #2  

Follow-­‐Up  Run  #3  

Apply  Line  Robust  StaDsDc  

Apply  threshold  

Clustering  

Visual  InspecDon  

Apply  threshold  

Apply  threshold  

Known  line  removal  

Threshold  on  cluster  

occupancy  

Page 17: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   17  

E@H  all  sky  search    

E@H  Follow-­‐Up  Run  #1  

E@H  Follow-­‐Up  Run  #2  

Follow-­‐Up  Run  #3  

Apply  Line  Robust  StaDsDc  

Apply  threshold  

Clustering  

Visual  InspecDon  

Apply  threshold  

Apply  threshold  

Known  line  removal  

Threshold  on  cluster  

occupancy  

Candidates  from  disturbed  bands.  Separate  ad-­‐hoc  

analysis  

Candidates  from  undisturbed  bands.  This  

analysis.    

Page 18: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   18  

E@H  all  sky  search    

E@H  Follow-­‐Up  Run  #1  

E@H  Follow-­‐Up  Run  #2  

Follow-­‐Up  Run  #3  

Apply  Line  Robust  StaDsDc  

Apply  threshold  

Clustering  

Visual  InspecDon  

Apply  threshold  

Apply  threshold  

Known  line  removal  

Threshold  on  cluster  

occupancy  

Directed  search  for  each  candidate:  Finer  sky  grids,  unchanged  Tcoh  

Ca  16  Mio  candidates  at  this  point    

Page 19: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   19  

E@H  all  sky  search    

E@H  Follow-­‐Up  Run  #1  

E@H  Follow-­‐Up  Run  #2  

Follow-­‐Up  Run  #3  

Apply  Line  Robust  StaDsDc  

Apply  threshold  

Clustering  

Visual  InspecDon  

Apply  threshold  

Apply  threshold  

Known  line  removal  

Threshold  on  cluster  

occupancy  Directed  search  for  each  candidate:  

longer  Tcoh  (140  h)  

Ca  6  Mio  candidates  at  this  point    

Page 20: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   20  

E@H  all  sky  search    

E@H  Follow-­‐Up  Run  #1  

E@H  Follow-­‐Up  Run  #2  

Follow-­‐Up  Run  #3  

Apply  Line  Robust  StaDsDc  

Apply  threshold  

Clustering  

Visual  InspecDon  

Apply  threshold  

Apply  threshold  

Known  line  removal  

Threshold  on  cluster  

occupancy  

Finer  grid  spacings  

Ca  500k  candidates  at  this  point  

Page 21: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  We  have  not  yet  performed  this  step  but  we  expect:  – No  survivors  or  a  handful  – For  these  survivors  a  longer  coherent  Dme-­‐baseline  search  (FU4),  even  just  fully  coherent  would  be  trivial  

– A  candidate  surviving  FU4  would  be  something  to  get  seriously  excited  about  

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   21  

Page 22: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  E@H  S6  all-­‐sky  post-­‐processing  to  be  finished  this  year  •  Focus  on  signal  detecDon  rather  than  ease  of  Upper  Limits  

determinaDon  •  Ways  to  improve  E@H  searches  in  the  future:  

–  AdvLIGO  data    –  Beter  theoreDcal  understanding  of  parameter-­‐space  mismatch  behaviour  (metric,  see  also    à  poster  by  Karl  Wete)  

–  Even  more  robust  detecDon  staDsDc  (including  transient  disturbance  &  signal  hypotheses)  à  see  poster  by  David  Keitel  

–  InformaDve  astrophysical  priors  (help,  please!)    –  Run  setup  as  decision  theory  problem  (“where  to  best  invest  our  money”)  à  poster  by  Jing  Ming  

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   22  

Page 23: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   23  

•  Bruce  Allen  •  David  Anderson  •  Stuart  Anderson    •  Carsten  Aulbert    •  Oliver  Bock  •  Christophe  Choquet  •  Jim  Cordes  •  Teviet  Creighton  •  Julia  Deneva  •  Heinz-­‐Bernd  

Eggenstein    •  Henning  Fehrmann    •  Akos  Fekete    •  Joachim  Fritzsch  

 

•  Steffen  Grunewald  •  Lucas  Guillemot    •  David  Hammer  •  Mike  Hewson    •  Yousuke  Itoh  •  David  Keitel  •  Gaurav  Khanna    •  Benjamin  Knispel    •  Badri  Krishnan  •  Paola  Leaci  •  Bernd  Machenschalk  •  Kathryn  Marks  •  Chris  Messenger  

•  Eric  Myers  

     

 •  Irene  Di  Palma  •  Maria  Alessandra  Papa  •  Ornella  Piccinni  •  Holger  Pletsch  •  Reinhard  Prix    •  Gary  Roberts    •  Miroslav  Shaltev    •  Peter  Shawhan    •  Xavier  Siemens  •  Sinéad  Walsh    •  Rom  Walton  •  Graham  Woan  •  >  400k  volunteers  so  far  

Page 24: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%
Page 25: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   25  

Client  sojware   Server  sojware  

E@H  server,  UWM  &  AEI  Hannover  

•  Provided  by  general  public  •  Ca  50k  PCs,  notebooks  &  smartphones    

acDvely  parDcipaDng  currently  •  MS  Windows,  OSX,  Linux,  Android    

CompuDng  tasks    (on  request)  

•  MulDple  searches  •  GW  •  Radio-­‐pulsar  search  •  Gamma-­‐ray  pulsar  search  

•  Each  task  send  to  two  different  parDcipants      

Results  sent  back  

Task  generaDon,    result  validaDon,    archival,  post-­‐  processing  &  follow  up  task  generaDon        

science  project-­‐    specific  back-­‐end  

Search  app.  download  

Internet  

Page 26: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  From  [LVC  (J.  Aasi  et  al.)  ,  Phys.Rev.  D87  (2013)  042001]  •  Upper  Uupper  Limits    

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   26  

Upper  limits  (at  90%  confidence  level)  

Page 27: Irene%Di%Palma ,%HeinzBernd% Eggenstein …...Irene%Di%Palma*,%HeinzBernd% Eggenstein*, David%Keitel*,%MariaAlessandraPapa *#, Sinéad%Walsh# *Max%Planck%InsDtute%for%Gravitaonal%Physics%%

•  Some  representaDve  examples  (not  from  actual  S6  data)  

•  Axes:    x:      ,  y:        ,  z  &  colour:                (detecDon  stat.)      

GWPAW  2015-­‐06-­‐19   LIGO  Doc  #  G1500557   27  

f f 2F