Relative Tuning of the Pythia Underlying Event for Recent PDFs

S. Muanza Simulation Meeting 16 September 2005

Relative Tuning of the Pythia Underlying Event for Recent PDFs

OUTLINE

I. Introduction and MethodologyII. Tools utilizedIII. Comparison MethodIV. Current ResultsV. Prospects


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.html)

• 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:

http://www.phys.ufl.edu/~rfield/cdf/rdf_talks.html












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)=4.0;PARP(82)=2.0 PARP(83)=0.5;PARP(84)=0.4 PARP(85)=0.9;PARP(86)=0.95

PARP(89)=1800.0;PARP(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


I. Introduction• This tuning is PDF dependent (http://cepa.fnal.gov/patriot/mc4run2/MCTuning/run2mc/R_Field.pdf)

• This tuning fits CDF Run IIA min.bias+soft jets data

• Provided decent choice for the renormalization scales, this tuning also fits the UE for the bbbar, di-photon, Z+jets processes (http://www.phys.ufl.edu/~rfield/cdf/RickField_Workshop_6-11-04.pdf)

http://cepa.fnal.gov/patriot/mc4run2/MCTuning/run2mc/R_Field.pdf










http://www.phys.ufl.edu/~rfield/cdf/RickField_Workshop_6-11-04.pdf

http://www.phys.ufl.edu/~rfield/cdf/RickField_Workshop_6-11-04.pdf


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 I’m 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)


I. Methodolgy

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

Scan over the UE Parameters

This scan contains 3888 different PARP configurations


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 MSEL=2 MSUB(91-95) elastic scattering+ diffraction + low pT QCD, w/ pT* > 0 GeVNote: the soft jets part is not yet produced ( MSEL=1, w/ pT* > 5 GeV)

• Statistics: 25k / sample (ie per PDF/ & per PARP combination)

• PDF: ref. sample:

• CTEQ5L (LO fit & LO S)

compar. sample: • CTEQ6LL, ALEKHIN02LO, MRST01LO (LO fit & LO S)• CTEQ6L (LO fit & NLO S)


• Events selection: • Similar to R. Field's:

events w/ 1 or 2 jets, pT(jets)> 0 GeV, |eta(jets)|<2.0• The transverse plane is divided into 4 regions:

• towards: |(ojbect,leading jet)|<60°• away: | (ojbect, leading jet)|>120° (only for 2 jet events)• transverse regions: 60°<|(ojbect,leading jet)|<120°

• Look at tracks w/ pT(tracks)>0.5 GeV and |eta(tracks)|<1.0 in the transverse regions• Construct 2-D histos:

• Ntracks//1 GeV) vs leading jet pT• pT/ /1 GeV) vs leading jet pT (scalar pT sum)

• Differences wrt R. Field: I used "calorimeter jets" instead of “track jets” => pT(jets)>6 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!!!

II. Tools Utilized


Charged Particle Correlations

Charged Jet #1Direction

“Transverse” “Transverse”

“Toward”

“Away”

“Toward-Side” Jet

“Away-Side” Jet

• Look at charged particle correlations in the azimuthal angle relative to the leading charged particle jet.

• Define || < 60o as “Toward”, 60o < || < 120o as “Transverse”, and

|| > 120o as “Away”.

• All three regions have the same size in - space, x = 2x120o = 4/3.

Charged Jet #1Direction

“Toward”

“Transverse” “Transverse”

“Away”

-1 +1

2

0

Leading Jet

Toward Region

Transverse Region

Transverse Region

Away Region

Away Region


Tuned PYTHIA 6.206 vs HERWIG 6.4 “TransMAX/MIN” vs PT(chgjet#1)

• Plots shows data on the “transMAX/MIN” <Nchg> and “transMAX/MIN” <PTsum> vs PT(chgjet#1). 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) > 3 GeV/c) and two tuned versions of PYTHIA 6.206 (PT(hard) > 0, CTEQ5L, PARP(67)=1 and PARP(67)=4).

<Nchg>

Charged Jet #1 Direction

“Toward”

“TransMAX” “TransMIN”

“Away” <PTsum>

"Max/Min Transverse" Nchg

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0 5 10 15 20 25 30 35 40 45 50

PT(charged jet#1) (GeV/c)

"Tra

nsv

erse

" <

Nch

g>

in

1 G

eV/c

bin

"Max Transverse"

"Min Transverse"

CDF Preliminarydata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

CTEQ5L

Tuned PYTHIA 6.206PARP(67)=1


HERWIG 6.4

"Max/Min Transverse" PTsum

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 5 10 15 20 25 30 35 40 45 50

PT(charged jet#1) (GeV/c)

<P

Tsu

m>

(G

eV/c

) in

1 G

eV/c

bin

"Max Transverse"

"Min Transverse"

CDF Preliminarydata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

CTEQ5L



HERWIG 6.4


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

)2(var)1(var))2(var&)1((varKSKSKSPPP

)2(var)2var|1(var))2(var&)1((varKSKSKSPPP

Valid if & only if var1 and var2 are not correlated!!!

Have to calculate a conditional probability if var1 and var2 are correlated!!!


IV. Current Results

• PDF: ALEKHIN02LO• LO fit and LO S

• 3881/3888 configs

UE Parameter Best Worst CTEQ5L

Tune APARP(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

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


IV. Current Results

• PDF: MRST01LO• LO fit and LO S

• 3820/3888 configs



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

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


IV. Current Results

• PDF: CTEQ6L• LO fit and NLO S

• 3867/3888 configs



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



IV. Current Results• PDF: CTEQ6LL

• aka CTEQ6L1• LO fit and LO S

• 3886/3888 configs



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



IV. Current Results

refbest

worst

« same »

Example w/ Alekhin 2002 LO PDF

HT (GeV)


IV. Current Results

refbest

worst

« same »

mET (GeV)


IV. Current Results

refbest

worst

« same »

N(jets)


IV. Current Results

refbest

worst

« same »

Total N(tracks)


IV. Current Results

• After fixing the correlation issue:

Attaching file plots.root as _file0...root [1] h2_hist1_mix0->GetCorrelationFactor(1,2) (const Stat_t)1.22289661104907951e-01root [2] h2_hist1_mix1->GetCorrelationFactor(1,2)(const Stat_t)8.79092032677424529e-01root [3] h2_hist2_mix0->GetCorrelationFactor(1,2) (const Stat_t)1.18224432124161408e-01root [4] h2_hist2_mix1->GetCorrelationFactor(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

Var1pT/Ntracks)/ /1 GeV)

Var1pT/ /1 GeV) Var2Ntracks//1 GeV)


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 (CTEQ5L+Tune 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


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


Back Up


Pythia UE Parameters Definition

UE Parameter DefinitionMSTP(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)~ECM^PARP(90)


VI. Final Checks on Shapes

Documents

Relative Tuning of the Pythia Underlying Event for Recent PDFs