Post on 16-Oct-2021
Fragmentationsfunktionen, Multiplizität und H1
Nicolas du Fresne von Hohenesche
Bonn Meeting 19.12.2012
COMPASS
Nicolas du Fresne von Hohenesche FF und H1
Semiinklusive tiefinelastische Streuung
Nachweis eines Hadrons h inKoinzidenz mit einem gestreutenLepton l �
l + N = l � + h + X
Zusätzliche Variablen: z, xf
Nicolas du Fresne von Hohenesche FF und H1
Fragmentationsfunktionen
Nicht farbneutrale Fragmente nach der Streuung vorhanden ⇒HadronisierungWelche Hadronen entstehen? Und wieviele? ⇒Fragmentationsfunktionen Dh
q(z): nicht normierteWahrscheinlichkeit, dass aus Quark q eine Hadron h entstehtmit dem Impulsbruchteil z⇒ FF sind Umkehrungen von Quarkverteilung q(x)Eigenschaften von Dh
q :�
h
� 1
0z · Dh
f (Q2, z)dz = 1
nh =�
f
� 1
zSchwelle
Dhf (Q
2, z)dz
Unabhängig vom Streuprozess (⇒ Unabhängigkeit von x)Universalität, d.h. unabhängig von der Produktionsart desQuark, das hadronisiert
Nicolas du Fresne von Hohenesche FF und H1
Multiplizität als Observable
Multiplizität für Hadron vom Typ h
1σtot
dσh
dz=
1Ntot
dNh
dz=
�q e2
qq(x)Dhq(z)�
q e2qq(x)
Gemessen werden Summen von Fragmentationsfunktion mitunterschiedlichen Gewichtungen
Nicolas du Fresne von Hohenesche FF und H1
Fragmentationsfunktion II
Es gibt viele Fragmentationsfunktionen, z.B.:Dπ+
u , Dπ−u , Dπ+
u , DK−u , Dπ0
u usw.Isospinsymmetrie und Ladungskonjugation:
Dπ+
u = Dπ+
d = Dπ−d = Dπ−
u → favoured
Dπ+
u = Dπ+
d = Dπ−
d = Dπ−u → unfavoured
Unterscheidung von favoured und unfavoured
Annahmen:Dfav > Dunfav
alle nonfavoured FF sind gleichalle favoured FF von leichten Quarks in Pionen sind gleich
Nicolas du Fresne von Hohenesche FF und H1
Herangehensweise
Standard Event-SelektionBest Primary VertexReconstructed µ and µ�
Target cutAuswahl des Hadrons
Stromquark (xf oder z)0.1 < x < 0.5 Valenzquarkregion oder Seequarkregion10 GeV/c2 < p < 50 GeV/c2 für Identifikation im RICH
⇒ Messen von Hadronen (π, K und p)
Nicolas du Fresne von Hohenesche FF und H1
Hadron-Identifikation: RICH
cosθ =1
n · β
Schwelleneffekt für verschiedene Teilchenarten (n, m)Verschmierung bei hohen ph
Nicolas du Fresne von Hohenesche FF und H1
H1 als Flugzeitdetektor
Ähnliche Akzeptanz wie RICHGute ZeitauflösungLimitation: Luftlichleiter, Loch
Nicolas du Fresne von Hohenesche FF und H1
TOF
Time-of-flight Methode als Teilchenidentifikation? Auflösung:
∆t =L
E1 − E2≈ Lc
2p2 (m21 − m2
2)
∆t = 4σt für K, π Trennung
Zutaten:(tj -ts) : Differenz(tj+ts)/2 : Meantimevscinti
KalibrationTrackingImpuls-Rekonstruktion
Nicolas du Fresne von Hohenesche FF und H1
RPD als Vorbild
TOF wird beim RPD verwendet (→ RPDhelper)Unterschiede: keine Ring A, SM1
Startcounter
Nicolas du Fresne von Hohenesche FF und H1
Test: FI01 als Startcounter
FI01 hitsVertex-Zeit mit v=c2012 Carbon-DatenVgl. mit Meantime (hier:Track-Time)
htemp3Entries 1827626Mean 0.5215RMS 1.592
-10 -8 -6 -4 -2 0 2 4 6 8 100
20
40
60
80
100
120
310× htemp3Entries 1827626Mean 0.5215RMS 1.592
timeFI01
htemp2Entries 411876Mean -49.6RMS 11.17
-70 -65 -60 -55 -50 -45 -40 -35 -30
1000
2000
3000
4000
5000
6000
htemp2Entries 411876Mean -49.6RMS 11.17
Zprim {Zprim>-70&& Zprim<-30}htemp1
Entries 411876Mean 27.13RMS 1.494
22 24 26 28 30 32 34 36 380
5000
10000
15000
20000
25000
30000
35000
htemp1Entries 411876Mean 27.13RMS 1.494
vertex_time {Zprim>-70&& Zprim<-30}
Nicolas du Fresne von Hohenesche FF und H1
Zeitkalibration
Hier meantime für schmallen Streifen in der Mitte (ExtrapolierteTracks)
0 5 10 15 20 25 30
-844
-842
-840
-838
-836
-834
-832
-830
-828
-826calibration
Entries 235705Mean x 15.7Mean y -836.5RMS x 5.48RMS y 2.04
200
400
600
800
1000
1200
1400
1600
1800
2000
calibrationEntries 235705Mean x 15.7Mean y -836.5RMS x 5.48RMS y 2.04
calibrationcalibration_1
Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
calibration_1Entries 26Mean 15.5RMS 8.12
Für Jura und SaleveNicolas du Fresne von Hohenesche FF und H1
Geschwindigkeit im Szintillator Saleve
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch2_1
Entries 230
Mean -0.6388
RMS 66.43
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_s_t_ch3_1
Entries 230
Mean -0.4753
RMS 66.43
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_s_t_ch4_1
Entries 229
Mean -0.2485
RMS 66.22
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch5_1
Entries 230
Mean -0.697
RMS 66.47
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch6_1
Entries 230
Mean -0.9024
RMS 66.29
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
-400
-300
Fitted value of par[1]=MeanHG01Y1_s_t_ch7_1
Entries 230
Mean -0.4782
RMS 66.34
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch8_1
Entries 230
Mean -0.6313
RMS 66.49
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch9_1
Entries 230
Mean -0.7511
RMS 66.46
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch10_1
Entries 230
Mean -0.7628
RMS 66.42
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch11_1
Entries 230
Mean -0.8026
RMS 66.48
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch12_1
Entries 230
Mean -0.7048
RMS 66.57
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch13_1
Entries 230
Mean -0.7003
RMS 66.6
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch14_1
Entries 230
Mean -0.7648
RMS 66.58
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_s_t_ch15_1
Entries 196
Mean 0.3675
RMS 71.82
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_s_t_ch16_1
Entries 196
Mean 0.02937
RMS 72.05
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch17_1
Entries 230
Mean -0.7071
RMS 66.62
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch18_1
Entries 230
Mean -0.8505
RMS 66.55
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch19_1
Entries 230
Mean -1.509
RMS 66.83
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1300
-1200
-1100
-1000
-900
-800
-700
Fitted value of par[1]=MeanHG01Y1_s_t_ch20_1
Entries 230
Mean -1.636
RMS 66.8
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1400
-1300
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch21_1
Entries 230
Mean -1.327
RMS 66.77
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_s_t_ch22_1
Entries 230
Mean -0.724
RMS 66.35
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_s_t_ch23_1
Entries 230
Mean -0.9654
RMS 66.55
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_s_t_ch24_1
Entries 230
Mean -0.996
RMS 66.61
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_s_t_ch25_1
Entries 230
Mean -0.7657
RMS 66.38
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch26_1
Entries 230
Mean -0.7171
RMS 66.51
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_s_t_ch27_1
Entries 230
Mean -0.6297
RMS 66.52
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_s_t_ch28_1
Entries 229
Mean -0.3703
RMS 66.26
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_s_t_ch29_1
Entries 230
Mean -0.6529
RMS 66.49
Fitted value of par[1]=Mean
Nicolas du Fresne von Hohenesche FF und H1
Geschwindigkeit im Szintillator Jura
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch2_1
Entries 230
Mean 0.7829
RMS 66.5
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch3_1
Entries 230
Mean 0.5321
RMS 66.49
Fitted value of par[1]=Mean
-100 -50 0 50 100-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch4_1
Entries 230
Mean 0.9778
RMS 66.59
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_j_t_ch5_1
Entries 230
Mean 0.8916
RMS 66.52
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch6_1
Entries 230
Mean 0.6262
RMS 66.48
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch7_1
Entries 230
Mean 0.6896
RMS 66.47
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch8_1
Entries 230
Mean 0.9791
RMS 66.54
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1400
-1200
-1000
-800
-600
-400
Fitted value of par[1]=MeanHG01Y1_j_t_ch9_1
Entries 230
Mean 0.9302
RMS 66.59
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
Fitted value of par[1]=MeanHG01Y1_j_t_ch10_1
Entries 230
Mean 0.7222
RMS 66.59
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch11_1
Entries 230
Mean 0.976
RMS 66.68
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch12_1
Entries 230
Mean 0.966
RMS 66.58
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1050
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_j_t_ch13_1
Entries 230
Mean 1.314
RMS 66.82
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1300
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_j_t_ch14_1
Entries 230
Mean 0.5921
RMS 66.48
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1400
-1200
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_j_t_ch15_1
Entries 196
Mean 2.074
RMS 72.12
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-800
-600
-400
-200
0
Fitted value of par[1]=MeanHG01Y1_j_t_ch16_1
Entries 196
Mean 1.851
RMS 72.1
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_j_t_ch17_1
Entries 230
Mean 0.814
RMS 66.63
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
-700
-650
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch18_1
Entries 230
Mean 0.7953
RMS 66.58
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1300
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_j_t_ch19_1
Entries 230
Mean 1.101
RMS 66.76
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_j_t_ch20_1
Entries 230
Mean 1.174RMS 66.72
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1400
-1200
-1000
-800
-600
-400
Fitted value of par[1]=MeanHG01Y1_j_t_ch21_1
Entries 230
Mean 1.776
RMS 66.82
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1300
-1200
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch22_1
Entries 230
Mean 1.176
RMS 66.62
Fitted value of par[1]=Mean
-100 -50 0 50 100
-160
-140
-120
-100
-80
-60
-40
-20
0
310×
Fitted value of par[1]=MeanHG01Y1_j_t_ch23_1
Entries 230
Mean 36.7
RMS 62.88
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch24_1
Entries 230
Mean 0.7569
RMS 66.55
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1100
-1000
-900
-800
-700
-600
-500
Fitted value of par[1]=MeanHG01Y1_j_t_ch25_1
Entries 230
Mean 0.6404
RMS 66.54
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1000
-800
-600
-400
Fitted value of par[1]=MeanHG01Y1_j_t_ch26_1
Entries 230
Mean 0.9517
RMS 66.55
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1200
-1100
-1000
-900
-800
-700
-600
Fitted value of par[1]=MeanHG01Y1_j_t_ch27_1
Entries 230
Mean 1.13
RMS 66.66
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1150
-1100
-1050
-1000
-950
-900
-850
-800
-750
-700
Fitted value of par[1]=MeanHG01Y1_j_t_ch28_1
Entries 230
Mean 1.139
RMS 66.61
Fitted value of par[1]=Mean
-100 -50 0 50 100
-1050
-1000
-950
-900
-850
-800
-750
-700
-650
Fitted value of par[1]=MeanHG01Y1_j_t_ch29_1
Entries 230
Mean 0.6918
RMS 66.46
Fitted value of par[1]=Mean
Nicolas du Fresne von Hohenesche FF und H1