PYTHIA Tune A versus Run 2 Data

1
PYTHIA Tune A versusRun 2 Data
Outline of Talk

• Compare PYTHIA Tune A with Run 2 data on the
  underlying event.

• Compare PYTHIA Tune A with the properties of the
  calorimeter jets as measured in Run 2.

Construct correction factors!

• Compare PYTHIA Tune A before and after CDFSIM.

• Use PYTHIA Tune A to correct the Run 2 data from
  measured to true.

JetClu R 0.7
2
Underlying Eventas defined by Charged
particle Jets
Look at the charged particle density in the
transverse region!
Charged Particle Df Correlations PT gt 0.5 GeV/c
h lt 1
Transverse region is very sensitive to the
underlying event!
Toward-side jet (always)
Perpendicular to the plane of the 2-to-2 hard
scattering
Away-side jet (sometimes)

• 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.

3
Tuned PYTHIA 6.206Run 1 Tune A
Describes the rise from Min-Bias to underlying
event!
Set A PT(charged jet1) gt 30 GeV/c Transverse
ltdNchg/dhdfgt 0.60
Min-Bias
Set A Min-Bias ltdNchg/dhdfgt 0.24

• Compares the average transverse charge particle
  density (hlt1, PTgt0.5 GeV) versus PT(charged
  jet1) and the PT distribution of the
  transverse and Min-Bias densities with the
  QCD Monte-Carlo predictions of a tuned version of
  PYTHIA 6.206 (PT(hard) gt 0, CTEQ5L, Set A).

Describes Min-Bias collisions!
Describes the underlying event!
4
Transverse Charged Particle Density
Transverse region as defined by the leading
charged particle jet
Excellent agreement between Run 1 and 2!

• Shows the data on the average transverse charge
  particle density (hlt1, PTgt0.5 GeV) as a
  function of the transverse momentum of the
  leading charged particle jet from Run 1.

• Compares the Run 2 data (Min-Bias, JET20, JET50,
  JET70, JET100) with Run 1. The errors on the
  (uncorrected) Run 2 data include both statistical
  and correlated systematic uncertainties.

PYTHIA Tune A was tuned to fit the underlying
event in Run I!

• Shows the prediction of PYTHIA Tune A at 1.96 TeV
  after detector simulation (i.e. after CDFSIM).

5
Transverse Charged PTsum Density
Transverse region as defined by the leading
charged particle jet
Excellent agreement between Run 1 and 2!

• Shows the data on the average transverse
  charged PTsum density (hlt1, PTgt0.5 GeV) as a
  function of the transverse momentum of the
  leading charged particle jet from Run 1.

• Compares the Run 2 data (Min-Bias, JET20, JET50,
  JET70, JET100) with Run 1. The errors on the
  (uncorrected) Run 2 data include both statistical
  and correlated systematic uncertainties.

PYTHIA Tune A was tuned to fit the underlying
event in Run I!

• Shows the prediction of PYTHIA Tune A at 1.96 TeV
  after detector simulation (i.e. after CDFSIM).

6
Underlying Eventas defined by Calorimeter
Jets
Charged Particle Df Correlations PT gt 0.5 GeV/c
h lt 1
Look at the charged particle density in the
transverse region!
Transverse region is very sensitive to the
underlying event!
Perpendicular to the plane of the 2-to-2 hard
scattering
Away-side jet (sometimes)

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

7
Transverse Charged Particle Density
Transverse region as defined by the leading
calorimeter jet

• Shows the data on the average transverse charge
  particle density (hlt1, PTgt0.5 GeV) as a
  function of the transverse energy of the leading
  JetClu jet (R 0.7, h(jet) lt 2) from Run 2.

, compared with PYTHIA Tune A after CDFSIM.

• Compares the transverse region of the leading
  charged particle jet, chgjet1, with the
  transverse region of the leading calorimeter
  jet (JetClu R 0.7), jet1.

8
Transverse Charged PTsum Density
Transverse region as defined by the leading
calorimeter jet

• Shows the data on the average transverse
  charged PTsum density (hlt1, PTgt0.5 GeV) as a
  function of the transverse energy of the leading
  JetClu jet (R 0.7, h(jet) lt 2) from Run 2.

, compared with PYTHIA Tune A after CDFSIM.

• Compares the transverse region of the leading
  charged particle jet, chgjet1, with the
  transverse region of the leading calorimeter
  jet (JetClu R 0.7), jet1.

9
Transverse Charged Particle Density
Transverse region as defined by the leading
calorimeter jet

• Shows the data on the average transverse charge
  particle density (hlt1, PTgt0.5 GeV) as a
  function of the transverse energy of the leading
  JetClu jet (R 0.7, h(jet) lt 2) from Run 2.

Small correction (about 10) independent of
ET(jet1)!
, compared with PYTHIA Tune A after CDFSIM.

• Shows the generated prediction of PYTHIA Tune A
  before CDFSIM.

• Shows the ratio CDFSIM/Generated for PYTHIA Tune
  A.

10
The Leading Charged Particle Jet

• Shows the data on the average number of charged
  particles within the leading charged particle
  jet (hlt1, PTgt0.5 GeV, R 0.7) as a function
  of the transverse momentum of the leading
  charged particle jet from Run 1.

Excellent agreement between Run 1 and 2!

• Compares the Run 2 data (Min-Bias, JET20, JET50,
  JET70, JET100) with Run 1. The errors on the
  (uncorrected) Run 2 data include both statistical
  and correlated systematic uncertainties.

PYTHIA produces too many charged particles in the
leading charged particle jet!
11
The Leading Calorimeter Jet

• Shows the Run 2 data on the average number of
  charged particles (hlt1, PTgt0.5 GeV, R 0.7)
  within the leading calorimeter jet (JetClu R
  0.7, h(jet)lt 0.7) as a function of the
  transverse energy of the leading calorimeter
  jet.

• Compares the number of charged particles within
  the leading charged particle jet, chgjet1,
  with the number of charged particles within the
  leading calorimeter jet (JetClu R 0.7), jet1.

PYTHIA produces too many charged particles in the
leading calorimeter jet!
12
The Leading Calorimeter JetCharged Particle
Multiplicity

• Shows the Run 2 data on the average number of
  charged particles (hlt1, PTgt0.5 GeV, R 0.7)
  within the leading calorimeter jet (JetClu R
  0.7, h(jet)lt 0.7) as a function of ET(jet1)
  compared with PYTHIA Tune A after CDFSIM.

Correction becomes large for ET(jet1) gt 100 GeV
and depends on ET(jet1)!
Multiply data by the unfolding function (i.e.
Generated/CDFSIM) determined from PYTHIA Tune A
to get corrected data.

• Shows the generated prediction of PYTHIA Tune A
  before CDFSIM.

• Shows the ratio CDFSIM/Generated for PYTHIA Tune
  A.

• Shows corrected Run 2 data compared with PYTHIA
  Tune A (uncorrected).

13
The Leading Calorimeter JetCharged PT
Distribution
The integral of F(z) is the average number of
charged particles within the leading charged
particle jet.

• Shows the transverse momentum distribution of
  charged particles (hlt1) within the leading
  calorimeter jet (JetClu, R 0.7, h(jet) lt
  0.7) compared with PYTHIA Tune A. The plot
  shows dNchg/dz with z PT/ET(jet1) for the
  range 30 lt ET(jet1) lt 70 GeV.

• Shows the transverse momentum distribution of
  charged particles (hlt1) within the leading
  charged particle jet compared with PYTHIA Tune
  A. The plot shows dNchg/dz with z
  PT/PT(chgjet1) for the range 30 lt PT(chgjet1) lt
  70 GeV/c.

PYTHIA produces too many soft charged particles
within the leading jet!
PYTHIA produces too many soft charged particles
within the leading jet!
14
The Leading Calorimeter JetCharged PTsum
PTmax Fraction

• Shows average charged PTsum fraction,
  PTsum/ET(jet1), and the average charged PTmax
  fraction, PTmax/ET(jet1), within the leading
  calorimeter jet (JetClu, R 0.7, h(jet) lt
  0.7) compared with PYTHIA Tune A.

• Shows distribution of the charged PTsum fraction,
  z PTsum/ET(jet1), and the distribution of
  charged PTmax fraction, z PTmax/ET(jet1),
  within the leading calorimeter jet (JetClu, R
  0.7, h(jet) lt 0.7) for the range 95 lt ET(jet1)
  lt 130 GeV compared with PYTHIA Tune A.

But PYTHIA does not do well on the charged PTsum
fraction!
But PYTHIA does not do as well on the charged
PTsum fraction!
PYTHIA does okay on the charged PTmax fraction!
PYTHIA does okay on the charged PTmax fraction!
15
The Leading Calorimeter JetCharged PTsum
Fraction

• Shows average charged PTsum fraction,
  PTsum/ET(jet1), within the leading calorimeter
  jet (JetClu, R 0.7, h(jet) lt 0.7) compared
  with PYTHIA Tune A after CDFSIM.

Very large correction that depends on ET(jet1)!
Multiply data by the unfolding function (i.e.
Generated/CDFSIM) determined from PYTHIA Tune A
to get corrected data.

• Shows the generated prediction of PYTHIA Tune A
  before CDFSIM.

• Shows the ratio CDFSIM/Generated for PYTHIA Tune
  A.

• Shows corrected Run 2 data compared with PYTHIA
  Tune A (uncorrected).

16
Proton-AntiProtonCollisions
Momentum perpendicular to the beam axis

• Draw an R 0.7 cone around the leading
  calorimeter jet (JetClu, R 0.7).

• Look at charged particles within R 0.7 of the
  leading calorimeter jet.

Momentum perpendicular to the jet axis
17
The Leading Calorimeter JetCharged KT
Distribution
Increases as ET(jet1) increases!

• Shows the average momentum perpendicular to the
  jet axis for charged particles (PT gt 0.5 GeV/c,
  hlt1) within the leading calorimeter jet
  (JetClu, R 0.7) compared with PYTHIA Tune A.

• Shows the distribution of momentum perpendicular
  to the jet axis for charged particles within the
  leading calorimeter jet compared with PYTHIA
  Tune A. The plot shows dNchg/dKT for the range 30
  lt ET(jet1) lt 70 GeV and 95 lt ET(jet1) lt 130 GeV.

18
Inclusive Jet Cross Section
Data and theory are normalized to agree at this
one point. This fixes the normalization for all
the other plots presented in this talk!
Very similar to Frank Chlebanas corrected
plots!

• Shows the uncorrected inclusive calorimeter jet
  cross-section for (JetClu, R 0.7, energy scale
  factor of 1.042) compared with PYTHIA Tune A
  (after CDFSIM).

• Shows the ratio of the uncorrected inclusive
  calorimeter jet cross-section for (JetClu, R
  0.7, energy scale factor of 1.042) to PYTHIA Tune
  A (after CDFSIM).

19
Inclusive Cross-SectionCorrection Factors
True
True
Correction factors!
Measured

• Shows PYTHIA Tune A CDFSIM inclusive
  calorimeter jet cross-section for (JetClu, R
  0.7) compared with the true cross-section where
  true is the PTsum of all hadrons (partons) with
  PT gt 0 in R 0.7 cone around JetClu.

20
Jet Cross Sections
Measures how much cross-section comes from gt1 jet!

• Shows the Run 2 uncorrected inclusive
  calorimeter jet cross-section and the leading
  calorimeter jet cross-section (JetClu, R 0.7,
  energy scale factor of 1.042).

• Shows the ratio of the leading jet cross section
  to the inclusive jet cross-section for (JetClu, R
  0.7, energy scale factor of 1.042) compared
  with PYTHIA Tune A (after CDFSIM).

21
Jet Cross Sections
Tansverse momentum of the hard 2-to-2
parton-parton collision!

• Shows the uncorrected inclusive jet cross-section
  for (JetClu, R 0.7, energy scale factor of
  1.042) compared with PYTHIA Tune A (after
  CDFSIM).

• Shows the ratio of the uncorrected inclusive jet
  cross-section for (JetClu, R 0.7, energy scale
  factor of 1.042) to PYTHIA Tune A (after CDFSIM).
  

22
Leading Charged Particle Jet Cross Section
Compares data/theory for the leading charged
particle jet and the leading calorimeter jet!

• Shows the uncorrected leading charged particle
  jet cross-section for (PT gt 0.5 GeV/c, hlt1)
  compared with PYTHIA Tune A (after CDFSIM).

• Shows the ratio of the uncorrected leading
  charged particle jet cross-section for (PT gt
  0.5 GeV/c, hlt1) to PYTHIA Tune A (after
  CDFSIM).

23
Summary Conclusions
PYTHIA Tune A

• PYTHIA Tune A does a good job of describing the
  underlying event in the Run 2 data as defined
  by charged particle jets and as defined by
  calorimeter jets. HERWIG Run 2 comparisons
  will be coming soon!
• PYTHIA Tune A does a fairly good job (although
  not perfect) describing the properties of the
  calorimeter jets in Run 2 (in the central
  region!).
• I am hoping we can use the QCD Monte-Carlo models
  (PYTHIA HERWIG) to correct the data from
  measured to true by constructing correction
  factors for every observable of interest.

This is a different method from the jet energy
corrections used in Run 1!