Moriond QCD 2002

1
Relative Tuning of the Pythia Underlying Event
for Recent PDFs

• OUTLINE
• Introduction and Methodology
• Tools utilized
• Comparison Method
• Current Results
• Prospects

2
I. Introduction

• A quite detailed study of the underlying event
  has been performed by Rick Field a theorist
  working in the CDF collaboration
  (http//www.phys.ufl.edu/rfield/cdf/rdf_talks.htm
  l)
• This study has been sustained for more than 5
  years
• Working definition of the Underlying Event
• All but the hard scattering process
• ie beam-beam remnants (spectator partons), plus
  possible ISR gluon radiations , plus the possible
  Multiple Parton Interactions (MPI)
• Systematic comparisons of CDF Run I data
  (min.bias and soft jets) to different MC models
  have been performed and finally led to a tuning
  of Pythia underlying event model

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I. Introduction
First Step Second Steps
Pythia Version 6.115 6.206
PDF CTEQ4L CTEQ5L
Tuning Name Tune 0 Tune A, B, C, D
MSTP Values MSTP(81)1 (MPI on) MSTP(82)4 (dble gauss. had. matter dens.) MSTP(81)1 MSTP(82)4
PARP Values PARP(82)2.4 (MPI pT cut-off) PARP(67)4.0PARP(82)2.0 PARP(83)0.5PARP(84)0.4 PARP(85)0.9PARP(86)0.95 PARP(89)1800.0PARP(90)0.25
Usage at D0 mcp10-mcp14 cardfiles/np/v00-02-01 to v00-04-58 mcp14 cardfiles/np/v00-04-59 to v00-08-53 cardfiles/dzero/v00-05-01 to v00-08-53
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I. Introduction

• This tuning is PDF dependent (http//cepa.fnal.go
  v/patriot/mc4run2/MCTuning/run2mc/R_Field.pdf)
• This tuning fits CDF Run IIA min.biassoft jets
  data
• Provided decent choice for the renormalization
  scales, this tuning also fits the UE for the
  bbbar, di-photon, Zjets processes
  (http//www.phys.ufl.edu/rfield/cdf/RickField_Wor
  kshop_6-11-04.pdf)

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I. Methodolgy

• Since the UE tuning is PDF dependent it should
  in principle be redone whenever changing from the
  reference PDF (CTEQ5L for Tune A)
• However this is obviously cumbersome since it
  requires correcting either the data or the
  detailed MC and re-doing the full tuning
  procedure each time
• I propose instead to start from a reference
  (CTEQ5L for Tune A) that was properly tuned to
  data and just to reproduce its UE properties
• This only requires generator level or fast
  simulation scan over the UE parameters whatever
  set of parameters that reproduces the reference
  UE constitutes the relative UE tuning for a given
  PDF
• I assume the p/pbar hadronic matter is described
  by a double gaussian (MSTP(82)4 as in Tune A),
  so Im left w/ scanning only over 7 PARP
  parameters (67,82-86,90) since PARP(89)1800.0
  keeps its fixed value (all the evolutions to
  another CoM energy are internally treated within
  Pythia)

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I. Methodolgy
Scan over the UE Parameters
UE Parameter Min Max Scan Step Default
PARP(67) 1.0 4.0 1.0 1.0
PARP(82) 1.8 2.1 0.1 2.0
PARP(83) 0.4 0.6 0.1 0.5
PARP(84) 0.3 0.5 0.1 0.2
PARP(85) 0.33 1.00 0.33 0.33
PARP(86) 0.33 1.00 0.33 0.66
PARP(90) 0.20 0.30 0.05 0.16
This scan contains 3888 different PARP
configurations
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II. Tools Utilized

• Generator
• Pythia v6.320
• PDF Library
• LHAPDF v4.0
• Fast detector simulation
• ATLFAST v2.60 (Atlas Collaboration), including
  smeared tracks and jets
• Events production
• Process
• Pythia minbias ? MSEL2 ? MSUB(91-95)
• ? elastic scattering diffraction low pT QCD,
  w/ pT gt 0 GeV
• Note the soft jets part is not yet produced (?
  MSEL1, w/ pT gt 5 GeV)
• Statistics
• 25k / sample (ie per PDF/ per PARP
  combination)
• PDF
• ref. sample
• CTEQ5L (LO fit LO aS)
• compar. sample
• CTEQ6LL, ALEKHIN02LO, MRST01LO (LO fit LO aS)
• CTEQ6L (LO fit NLO aS)

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II. Tools Utilized

• Events selection
• Similar to R. Field's
• events w/ 1 or 2 jets, pT(jets)gt 0 GeV,
  eta(jets)lt2.0
• The transverse plane is divided into 4 regions
• towards Df(ojbect,leading jet)lt60
• away Df(ojbect, leading jet)gt120 (only for
  2 jet events)
• transverse regions 60ltDf(ojbect,leading
  jet)lt120
• Look at tracks w/ pT(tracks)gt0.5 GeV and
  eta(tracks)lt1.0 in the transverse regions
• Construct 2-D histos
• Ntracks/Dh/Df/(1 GeV) vs leading jet pT
• SpT/ Dh/Df/(1 GeV) vs leading jet pT
  (scalar pT sum)
• Differences wrt R. Field
• I used "calorimeter jets" instead of track jets
  
• gt pT(jets)gt6 Gev instead of 0 GeV
• Note
• the overall efficiency is rather low (12) and
  since I did not write any filter for the
  produced events, the comparisons are only based
  on a KS test of two 2-D histos w/ 3 k entries!!!

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Charged Particle DfCorrelations

• Look at charged particle correlations in the
  azimuthal angle Df relative to the leading
  charged particle jet.
• Define Df lt 60o as Toward, 60o lt Df lt 120o
  as Transverse, and
• Df gt 120o as Away.
• All three regions have the same size in h-f
  space, DhxDf 2x120o 4p/3.

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Tuned PYTHIA 6.206 vs HERWIG 6.4 TransMAX/MIN
vs PT(chgjet1)
ltNchggt
ltPTsumgt

• Plots shows data on the transMAX/MIN ltNchggt and
  transMAX/MIN ltPTsumgt vs PT(chgjet1). The solid
  (open) points are the Min-Bias (JET20) data.
• The data are compared with the QCD Monte-Carlo
  predictions of HERWIG 6.4 (CTEQ5L, PT(hard) gt 3
  GeV/c) and two tuned versions of PYTHIA 6.206
  (PT(hard) gt 0, CTEQ5L, PARP(67)1 and PARP(67)4).

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III. Comparison Method

• Histo Comparisons for each PARP configuration
  and for each PDF, the two
• 2-D histos are compared using a 2-D
  Kolmogorov-Smirnov test to those of the ref.
  sample (just the shapes enter the comparison, not
  the normalizations)
• Global probability the probability assigned to
  each comparison sample is simply the product 1
  of the individual probability of comparing on one
  hand the charged tracks density and on the other
  hand the pTsum density
• Tools all the histos and comparison methods are
  taken from ROOT v4.04.02b

Valid if only if var1 and var2 are not
correlated!!!
Have to calculate a conditional probability if
var1 and var2 are correlated!!!
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IV. Current Results

• PDF ALEKHIN02LO
• LO fit and LO aS
• 3881/3888 configs

• Max(PKS)0.967 008 (8 max configs)
• Min(PKS)2.058x10-10 (4 min configs)

UE Parameter Best Worst CTEQ5L Tune A
PARP(67) FLAT FLAT 4.0
PARP(82) 2.0 1.8 2.0
PARP(83) 0.6 0.4 0.5
PARP(84) 0.3 0.4 0.4
PARP(85) 0.66 0.33 0.9
PARP(86) 0.33-0.66 1.0 0.95
PARP(90) 0.30 0.30 0.25
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IV. Current Results

• PDF MRST01LO
• LO fit and LO aS
• 3820/3888 configs

• Max(PKS)0.956524 (12 max configs)
• Min(PKS)4.433x10-11 (4 min configs)

UE Parameter Best Worst CTEQ5L Tune A
PARP(67) FLAT FLAT 4.0
PARP(82) 1.80 1.90 2.0
PARP(83) 0.6 0.4 0.5
PARP(84) 0.3 0.5 0.4
PARP(85) 1.0 0.33 0.9
PARP(86) FLAT 1.0 0.95
PARP(90) 0.20 0.20 0.25
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IV. Current Results

• PDF CTEQ6L
• LO fit and NLO aS
• 3867/3888 configs

• Max(PKS)0.954313 (12 max configs)
• Min(PKS)2.924x10-10 (4 min configs)

UE Parameter Best Worst CTEQ5L Tune A
PARP(67) FLAT FLAT 4.0
PARP(82) 2.10 1.80 2.0
PARP(83) 0.6 0.4 0.5
PARP(84) 0.5 0.4 0.4
PARP(85) 1.0 0.33 0.9
PARP(86) FLAT 1.0 0.95
PARP(90) 0.25 0.30 0.25
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IV. Current Results

• PDF CTEQ6LL
• aka CTEQ6L1
• LO fit and LO aS
• 3886/3888 configs

• Max(PKS)0.977060 (12 max configs)
• Min(PKS)1.815x10-11 (4 min configs)

UE Parameter Best Worst CTEQ5L Tune A
PARP(67) FLAT FLAT 4.0
PARP(82) 2.00 2.00 2.0
PARP(83) 0.4 0.4 0.5
PARP(84) 0.5 0.4 0.4
PARP(85) 1.0 0.33 0.9
PARP(86) FLAT 1.0 0.95
PARP(90) 0.20 0.30 0.25
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IV. Current Results
Example w/ Alekhin 2002 LO PDF
ref
best
worst
 same 
HT (GeV)
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IV. Current Results
ref
best
worst
 same 
mET (GeV)
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IV. Current Results
ref
best
worst
 same 
N(jets)
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IV. Current Results
ref
best
worst
 same 
Total N(tracks)
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IV. Current Results

• After fixing the correlation issue
• Attaching file plots.root as _file0...
• root 1 h2_hist1_mix0-gtGetCorrelationFactor(1,2)
  
• (const Stat_t)1.22289661104907951e-01
• root 2 h2_hist1_mix1-gtGetCorrelationFactor(1,2)
• (const Stat_t)8.79092032677424529e-01
• root 3 h2_hist2_mix0-gtGetCorrelationFactor(1,2)
  
• (const Stat_t)1.18224432124161408e-01
• root 4 h2_hist2_mix1-gtGetCorrelationFactor(1,2)
• (const Stat_t)8.23833825978842360e-01
• The correlation coefficient drops from 80
  downto 12
• This makes the marginal probabilities product an
  acceptable approximation

Var1SpT/ Dh/Df/(1 GeV)
Var2Ntracks/Dh/Df/(1 GeV)
Var1(SpT/Ntracks)/ Dh/Df/(1 GeV)
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VI. Conclusions Prospects

• Conclusions
• There are flat directions (as expected in
  multivariate analyses, especially w/ coarse scans
  and limited statistics). In this case I propose
  to pick the PARP value which is the closest to
  the reference one (CTEQ5LTune A)
• As expected the shape of the so-called best
  configuration (green histos) is the closest to
  that of the reference (black histos). This
  demonstate that there is a measurable difference
  between different UE settings for a given PDF and
  that the UE is PDF-dependent.
• Prospects
• Produce the low pT QCD samples
• Add them to the 2-D histos for the comparisons
• Couple of additional cross checks
• Increase the statistics

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VI. Prospects

• Produce the low pT QCD samples
• Add them to the 2-D histos for the comparisons
• Couple of additional cross checks
• Increase the statistics

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Back Up
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Pythia UE Parameters Definition
UE Parameter Definition
MSTP(81) MPI on/off
MSTP(82) 3 / 4 resp. single or double gaussian hadronic matter distribution in the p / pbar
PARP(67) ISR Max Scale Factor
PARP(82) MPI pT cut-off
PARP(83) Warm-Core parp(83) of matter in radius parp(84)
PARP(84) Warm-Core
PARP(85) prob. that an additional interaction in the MPI formalism gives two gluons, with colour connections to NN in momentum space
PARP(86) prob. that an additional interaction in the MPI formalism gives two gluons, either as described in PARP(85) or as a closed gluon loop. Remaining fraction is supposed to consist of qqbar pairs.
PARP(89) ref. energy scale
PARP(90) energy rescaling term for PARP(81-82)ECMPARP(90)
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VI. Final Checks on Shapes