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QCD Event shape variables in pp collision at 900/2360 GeV

• Introduction

• Minbias Models

• Data-set and event/track selection

• Event shape variables in Data and systematics

• Comparison of different models

• Conclusion

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Introduction

• Event shape variables widely used in ee/ep machine to tune MC (non-perturbative QCD effect)

• An attempt to look in pp collider for better QCD model

Variables under study :

This study is based on only tracker information, with the assumption of zero mass and P_Z=0

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Minbias models

• DW (Rick Field, ref : hep-ph/0201192) : Mainly tuning of CDF underling events, charge multiplicity, scalar sum in transverse plane, tuning PYTHIA, e.g.,– 'MSTJ(11)=3 ! Choice of the fragmentation function' (light quark in symmetric

fuction and heavy quark with Peterson/SLAC functionm), D=4 – 'MSTP(81)=1 ! multiple parton interactions 1 is Pythia default', D=1 – 'MSTP(82)=4 ! Defines the multi-parton model', D=4 – 'PARP(82)=1.9 ! pt cutoff for multiparton interactions', D=2.0 GeV – 'PARP(83)=0.5 ! Multiple interactions: matter distrbn parameter', D=0.5 – 'PARP(84)=0.4 ! Multiple interactions: matter distribution parameter', D=0.4 – 'PARP(90)=0.25 ! Multiple interactions: rescaling power', D=0.16 – 'PARP(67)=2.5 ! amount of initial-state radiation', D=4.0 – 'PARP(85)=1.0 ! gluon prod. mechanism in MI, prob of additional

interaction...', D=0.9 – 'PARP(86)=1.0 ! gluon prod. mechanism in MI' prob of additional

interaction...', D=0.95 – 'PARP(62)=1.25 ! Effective cut-off Q or K_T ', D=1.0 GeV – 'PARP(64)=0.2 ! Transverse momentum evolution scale ..', D=1.0 – 'PARP(91)=2.1 ! 354 kt distribution', D=2.0 – 'PARP(93)=15.0 ! 355 Upper cut-off of K_T'), D=5.0

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Minbias models• Professor’s model (ref : arXiv:0902.4403) : Based on DELPHI tuning• FSR and hadronisation from e+e− data

– 'PARJ(1)=0.073 ! FLAV P(qq)/P(q) D=0.10 – 'PARJ(2)=0.2 ! FLAV P(s)/p(u) D=0.30 – 'PARJ(3)=0.94 ! FLAV (P(us)/P(ud))/(P(s)/P(d)) D= 0.4 – 'PARJ(4)=0.032 ! FLAV Suppression of spin 1 wrt spin 0, D=0.05 – 'PARJ(11)=0.31 ! FLAV prob of spin 1 (ud) meson, D=0.5 – 'PARJ(12)=0.4 ! FLAV Prob. a strange meson has spine 1, D=0.6 – 'PARJ(13)=0.54 ! FLAV Prob of spine 1 (in heavier meson) D=0.75– 'PARJ(25)=0.63 ! FLAV Extra suppression factor for eta, D=1.0 – 'PARJ(26)=0.12 ! FLAV Extra suppression factor for eta', D=0.4 – 'MSTJ(11)=5 ! HAD Choice of the fragmentation function', Lund, but

interpolation between Bowler and Lund ... D=4 (lund) – 'PARJ(21)=0.313 ! HAD', Gaussian width of parton Pt smearing inside hadron

D=0.36– 'PARJ(41)=0.49 ! HAD', 'a' of symmetri Lund fragmentation function, D=0.3– 'PARJ(42)=1.2 ! HAD', 'b' of symmetri Lund fragmentation function, D=0.58– 'PARJ(46)=1.0 ! HAD', rC id Bowler shape D=1.0– 'PARJ(47)=1.0 !408 HAD', rB id Bowler shape D=1.0

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Minbias models : Professors

• Professor’s tuned ISR, FSR parameters are default in PYTHIA

• Also parameter’s are tuned for color reconnection, branching fractions (same is true for Perugia)

• ISR and MPI from pp-bar data

– 'MSTP(81)=1 (2) ! MPI 21 is Pythia new set of MPI models', D=1

– 'MSTP(82)=4 (5) ! MPI model, structure', D=4

– 'PARP(82)=1.9 (2.0) ! MPI pt cutoff for multiparton interactions', D=2.0

– 'PARP(83)=0.6 (1.7) ! MPI matter distribution parameter', O(b) \prop exp(-b^d), D=0.5

– 'PARP(90)=0.22 (0.26) ! MPI rescaling power', D=0.16

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Minbias models• Perugia model (Ref : arXiv:0905.3418)

– FSR and hadronisation from e+e− data (same as Professor’s)– ISR and K_T : Drell-Yan PT spectrum at Tevtron (√S= 1800 + 1960 GeV)– Underlying events (UE) and Beam Remnants (BR) : Charge multiplicity (Nch),

dNch/dPT, <PT>(Nch) in min-bias events at CDF– Energy Scaling : Nch in min-bias events at 200,540 and 900 GeV (UA5) + 630

+ 1800 GeV (CDF)

• FSR :– 'PARJ(81)=0.257 ! FSR', Lambda value in running alpha_S, D=0.29 – 'PARJ(82)=0.8 ! FSR', Invariant mass cut-off m_min in PYSHOW, D=1.0– 'PARP(71)=2.0 ! FSR', .. maximum parton virtuality, D=4.0

• ISR :– 'MSTP(64)=3 ! ISR', Choice of Alpha_s and Q2 for space-like parton showers

D=2 – 'PARP(64)=1.0 ! ISR', evolution scale multiplied by parp(62), D=1.0– 'MSTP(67)=2 ! ISR', Colour coherence effect, D=2 – 'PARP(67)=1.0 ! ISR', Q2 scale of the hard scattering (MATP(32) is multiplied

by this, D=4 – 'MSTP(70)=2 ! ISR', Regularision scheme when Pt->0, D=1– 'MSTP(72)=1 ! ISR', Maximum scale for FSR stretch between ISR ... D=1

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Data sets and event selection

• Data : /MinimumBias/BeamCommissioning09-Jan29ReReco-v2/RECO

• MC : /MinBias/Summer09-STARTUP3X_V8O_2360GeV_Jan29ReReco-v1/GEN-SIM-RECO            /MinBias/Summer09-STARTUP3X_V8P_900GeV_Jan29ReReco-v1/GEN-SIM-RECO/MinBias/Summer09-STARTUP3X_V8K_900GeV_DW-v1/GEN-SIM-RECO/MinBias/Summer09-STARTUP3X_V8K_900GeV_P0-v1/GEN-SIM-RECO/MinBias/Summer09-STARTUP3X_V8K_900GeV_ProQ20-v1/GEN-SIM-RECO

Due to crab problem, we do not have full sample

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Event selection and track selection (tried to use similar crit of UE selection)

• Only one primary vertex

• Vertex position, |Δr|<0.15 and |ΔZ|<15cm wrt nominal interaction vertex

• Fraction of high purity tracks > 25%

• Number of reconstructed tracks < 150

• BSC trigger bit 40,41 : HLT_MinBiasBSC, beamhalo veto and BPTX+ && BPTX− ,technical trigger bit 0) (for data only)

• Associated with primary vertex and weight in vertex fit> 0.2

• PT > 750 MeV, |η|<2.2 (study on the threshold, range as systematic)

• quality(TrackBase::highPurity)

• |d0|<0.1cm, |dz|<0.1cm wrt event vertex, ndf>=10, atleast one silicon layer hit and Track fit prob > 10−8

Finally, at least 3 selected tracks in the event

Track selection :

Event selection :

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Beam Monitors

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Run/Lumi/bunch selection

• Again used the same events as it is in UE analysis

• But also looked for events with Run # <123900 and (Early)

• Lumi/bunch rejected in UE selection (Others)

• 2360 data

    //Good run/lumi/branch *  (irun==124009 && ilumi>=1 && ilumi<=68 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124020 && ilumi>=12 && ilumi<=94 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124022 && ilumi>=69 && ilumi<=160 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124023 && ilumi>=41 && ilumi<=96 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124024 && ilumi>=2  && ilumi<=83 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124027 && ilumi>=24 && (ibrnc==2824||ibrnc==151||ibrnc==51)); *   (irun==124030 && ilumi>=1  && ilumi<=31 && (ibrnc==2824||ibrnc==151||ibrnc==51)); * (irun==124230 && ilumi>=26 && ilumi<=68 && (ibrnc==51 ||ibrnc==151 ||ibrnc==232 || ibrnc==1042 || ibrnc==1123 || ibrnc==1933 ||ibrnc==2014 ||ibrnc==2824 ||ibrnc==2905));

No luminosity, efficiency correction …, only comparison of shape

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Comparison of primary vertex parameters

Data and MC does not match in track multiplicity as well as primary vertex resolution

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Comparison of track parameters in Data and MC

• Impact parameters are better for MC sample

• Track fit probability is also better in MC

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Pt distribution of track for different |η| range

• Track multiplicity has large difference in Data and MC

• Nearly same as it is number of associated tracks with the primary vertex

• Tracking efficiency is less in forward direction

|η|<2.5|η|<2.2

|η|<1.9|η|<1.6

Not much dependency on eta : Perugia model has poorer matching

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Pt distribution of track for different |η| range (zoomed)

|η|<2.5|η|<2.2

|η|<1.9

|η|<1.6

Not much dependency on eta : Perugia model has poorer matching

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Pseudorapidity of track for different Pt threshold

Pt>1.0

Pt>0.3Pt>0.5

Pt>1.5

Better Data/MC matching with higher Pt threshold

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Number of selected tracks for different Pt & |η| criteria

Pt>0.5

|η|<2.5

Pt>1.0

|η|<2.5

Pt>0.5

|η|<2.0

Pt>1.0

|η|<2.0

Close match with higher Pt threshold, but different models vary different way.At high Pt threshold Perugia and Professor’s model show better matchings.

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Comparison of track parameters in Data and MC

• χ2/ndf : from ChisqTestX() of MC samples wrt to data

• Tracking efficiency is less in forward direction

• Pt distribution for Perugia minbias model is different than data/other models, but better interms of track multiplicity

Variables after all selection criteria

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Track parameters : Different data sets

• Looks data in different run periods

• Distributions in different datasets are nearly same

• Bottom plots : Ratio wrt 1st plots,

• Error is statistical only

Top one in logarithamic scale, whereas ratio’s are in linear scale

<χ2> : rms deviation of ratio from 1, normalised with error

Pr : Prob (chisquare, ndf)

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Track parameters : Different data sets

• Distributions in different datasets are nearly same

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Systematic

• Change in distributions in different run

– Divided data in four parts,

• UE event selection excluding run 124330

• Run 124330

• Run 124009 – 124330, but lumi/bunch is not selected in UE

• Initial runs, < 123900

• Change due to range in eta (arbitrary)

– |η|< 2.0, 2.1, 2.2, 2.3, 2.4

• Change due to different Pt threshold (arbitrary)

– Pt > 0.70, 0.75, 0.80 GeV

Problem : Statistical error is not negligible, it is added quadratically with relative shift

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Event Shape parameters : Different data sets

• Distributions in different datasets are nearly same

• Error in each bin for systematic : Maximum in three data sets, sqrt(shift^2 + error^2)

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Event Shape parameters : Different data sets

• Distributions in different datasets are nearly same

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Event Shape parameters : Different |η| range

• Not much variation due to different range of |η|

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Event Shape parameters : Different |η| range

• Not much variation due to different range of |η|

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Event Shape Variables : Comparison of Data and MC

All four models are deviating from data, need MC tuning. Perugia is slightly better than others

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Event Shape Variables : Comparison of Data and MC

All four models are deviating from data

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Correlation of tracks : Comparison of Data and MC

−Ve for opposite sign tracks : All four models are deviating from data, Professor-Q has better agreement for ΔPt, whereas Perugia for ΔΦ

Binwidths for Pt distribution are not const

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Correlation of tracks : Comparison of Data and MC

−Ve for opposite sign tracks : All four models are deviating from data, again ProQ has better agreement for ΔP, whereas P0 for Opening angle

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Difference in azimuthal angles of selected tracks

Pt>0.5

|η|<2.5

Pt>1.0

|η|<2.5

Pt>0.5

|η|<2.0

Pt>1.0

|η|<2.0

−ve sign is for opposite sign tracksPerugia model shows better matching than others two

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Difference in Pt of selected tracks

Pt>0.5

|η|<2.5

Pt>1.0

|η|<2.5

Pt>0.5

|η|<2.0

Pt>1.0

|η|<2.0

Perugia is poorer in comparison with others, opposite to ΔΦ correlation

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χ2 comparison of different models, with different Pt, |η| criteria

10 |η| bin from 2.5 to 1.6 and 18 Pt bin from 0.3 to 2.0 GeV

Matching of Data/MC varies with selection, mainly on Pt, very less on etaPerugia model : Much more different than others

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χ2 comparison of different models, , with different Pt, |η| criteria

5 |η| bin < 2.4 to 1.6

Perugia model is has better matching with data. For different threshold statistics are different, but variations in different models are different

10 Pt bins >[0.5, 0.6, 0.65, 0.7, 0.75, 0.8, 0.9, 1.0, 1.5, 2.0] GeV

QCD event shape variables

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Comparison of primary vertex parameters : 2360 GeV

Data and MC does not match in track multiplicity as well as primary vertex resolution

Beam position is much precise at high energy

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Comparison of track parameters in Data and MC : 2360 GeV

• Impact parameters and track fit probability are better for MC sample, but same for both beam energy

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Comparison of track parameters in Data and MC : 2360 GeV

• Expected increase in multiplicity and average Pt of track with beam energy

Variables after all selection criteria

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Event Shape variables : Comparison of Data and MC at 2360 GeV

• Same discrepancy in Data and MC, what was seen at 900 GeV, again track multiplicity is major concern.

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Conclusion

• Data and MC differ in track multiplicity, which can effect the distributions of QCD event shape variables

• Minbias models are not consistent with data, Perugia is closer to Data, but discrepancy is ~10% level (excluding normalisation factor)

• Correlation of Pt and Φ of tracks are different in different models.

• 2.36TeV data also show the same discrepancy.

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Event Shape parameters : Different Pt threshold

• Large dependency the threshold. Threshold mainly change the number of tracks in a events, which eventually change shapes

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Event Shape parameters : Different Pt threshold

• Large dependency the threshold. Threshold mainly change the number of tracks in a events, which eventually change shapes

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(RadFET)