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Title: University of Chicago


1
University of Chicago
Lecture 3 Tuning the Models and Extrapolations
to the LHC
Rick Field University of Florida
Enrico Fermi Institute, University of Chicago
CDF Run 2
2
Particle Densities
Charged Particles pT gt 0.5 GeV/c h lt 1
CDF Run 2 Min-Bias
DhDf 4p 12.6
CDF Run 2 Min-Bias Observable Average Average Density per unit h-f
Nchg Number of Charged Particles (pT gt 0.5 GeV/c, h lt 1) 3.17 /- 0.31 0.252 /- 0.025
PTsum (GeV/c) Scalar pT sum of Charged Particles (pT gt 0.5 GeV/c, h lt 1) 2.97 /- 0.23 0.236 /- 0.018
  • Study the charged particles (pT gt 0.5 GeV/c, h
    lt 1) and form the charged particle density,
    dNchg/dhdf, and the charged scalar pT sum
    density, dPTsum/dhdf.

3
Transverse Particle Densities
Charged Particles pT gt 0.5 GeV/c h lt 1
Area 4p/6
  • Study the charged particles (pT gt 0.5 GeV/c, h
    lt 1) in the Transverse 1 and Transverse 2 and
    form the charged particle density, dNchg/dhdf,
    and the charged scalar pT sum density,
    dPTsum/dhdf.
  • The average transverse density is the average
    of transverse 1 and transverse 2.

4
QCD Monte-Carlo Models High Transverse Momentum
Jets
Underlying Event
  • Start with the perturbative 2-to-2 (or sometimes
    2-to-3) parton-parton scattering and add initial
    and final-state gluon radiation (in the leading
    log approximation or modified leading log
    approximation).
  • The underlying event consists of the beam-beam
    remnants and from particles arising from soft or
    semi-soft multiple parton interactions (MPI).

The underlying event is an unavoidable
background to most collider observables and
having good understand of it leads to more
precise collider measurements!
  • Of course the outgoing colored partons fragment
    into hadron jet and inevitably underlying
    event observables receive contributions from
    initial and final-state radiation.

5
Evolution of Charged Jets Underlying Event
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 very sensitive to the
underlying event!
CDF Run 1 Analysis
  • 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.

6
Run 1 PYTHIA Tune A
CDF Default!
PYTHIA 6.206 CTEQ5L
Parameter Tune B Tune A
MSTP(81) 1 1
MSTP(82) 4 4
PARP(82) 1.9 GeV 2.0 GeV
PARP(83) 0.5 0.5
PARP(84) 0.4 0.4
PARP(85) 1.0 0.9
PARP(86) 1.0 0.95
PARP(89) 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25
PARP(67) 1.0 4.0
Run 1 Analysis
  • Plot shows the transverse charged particle
    density versus PT(chgjet1) compared to the QCD
    hard scattering predictions of two tuned versions
    of PYTHIA 6.206 (CTEQ5L, Set B (PARP(67)1) and
    Set A (PARP(67)4)).

Old PYTHIA default (more initial-state radiation)
Old PYTHIA default (more initial-state radiation)
New PYTHIA default (less initial-state radiation)
New PYTHIA default (less initial-state radiation)
7
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).

8
The Transverse Regions as defined by the
Leading Jet
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!
  • Look at charged particle correlations in the
    azimuthal angle Df relative to the leading
    calorimeter jet (JetClu R 0.7, h lt 2).
  • Define Df lt 60o as Toward, 60o lt -Df lt 120o
    and 60o lt Df lt 120o as Transverse 1 and
    Transverse 2, and Df gt 120o as Away. Each
    of the two transverse regions have area DhDf
    2x60o 4p/6. The overall transverse region is
    the sum of the two transverse regions (DhDf
    2x120o 4p/3).

9
Charged Particle Density Df Dependence
Refer to this as a Leading Jet event
Subset
Refer to this as a Back-to-Back event
  • Look at the transverse region as defined by the
    leading jet (JetClu R 0.7, h lt 2) or by the
    leading two jets (JetClu R 0.7, h lt 2).
    Back-to-Back events are selected to have at
    least two jets with Jet1 and Jet2 nearly
    back-to-back (Df12 gt 150o) with almost equal
    transverse energies (ET(jet2)/ET(jet1) gt 0.8)
    and with ET(jet3) lt 15 GeV.
  • Shows the Df dependence of the charged particle
    density, dNchg/dhdf, for charged particles in the
    range pT gt 0.5 GeV/c and h lt 1 relative to
    jet1 (rotated to 270o) for 30 lt ET(jet1) lt 70
    GeV for Leading Jet and Back-to-Back events.

10
Transverse PTsum Density vs ET(jet1)
Leading Jet
Back-to-Back
Min-Bias 0.24 GeV/c per unit h-f
  • Shows the average charged PTsum density,
    dPTsum/dhdf, in the transverse region (pT gt 0.5
    GeV/c, h lt 1) versus ET(jet1) for Leading
    Jet and Back-to-Back events.
  • Compares the (uncorrected) data with PYTHIA Tune
    A and HERWIG (without MPI) after CDFSIM.

11
Latest CDF Run 2 Underlying Event Results
The underlying event consists of the beam-beam
remnants and possible multiple parton
interactions, but inevitably received
contributions from initial and final-state
radiation.
Transverse region is very sensitive to the
underlying event!
Latest CDF Run 2 Results (L 385 pb-1)
  • Two Classes of Events Leading Jet and
    Back-to-Back.
  • Two Transverse regions transMAX, transMIN,
    transDIF.
  • Data Corrected to the Particle Level unlike our
    previous CDF Run 2 underlying event analysis
    which used JetClu to define jets and compared
    uncorrected data with the QCD Monte-Carlo models
    after detector simulation, this analysis uses the
    MidPoint jet algorithm and corrects the
    observables to the particle level. The corrected
    observables are then compared with the QCD
    Monde-Carlo models at the particle level.
  • For the 1st time we study the energy density in
    the transverse region.

12
TransMAX/MIN PTsum Density PYTHIA Tune A vs
HERWIG
PYTHIA Tune A does a fairly good job fitting the
PTsum density in the transverse region! HERWIG
does a poor job!
Back-to-Back
Leading Jet
  • Shows the charged particle PTsum density,
    dPTsum/dhdf, in the transMAX and transMIN
    region (pT gt 0.5 GeV/c, h lt 1) versus PT(jet1)
    for Leading Jet and Back-to-Back events.
  • Compares the (corrected) data with PYTHIA Tune A
    (with MPI) and HERWIG (without MPI) at the
    particle level.

13
TransMAX/MIN ETsum Density PYTHIA Tune A vs
HERWIG
Back-to-Back
Leading Jet
Neither PY Tune A or HERWIG fits the ETsum
density in the transferse region! HERWIG does
slightly better than Tune A!
  • Shows the data on the tower ETsum density,
    dETsum/dhdf, in the transMAX and transMIN
    region (ET gt 100 MeV, h lt 1) versus PT(jet1)
    for Leading Jet and Back-to-Back events.
  • Compares the (corrected) data with PYTHIA Tune A
    (with MPI) and HERWIG (without MPI) at the
    particle level (all particles, h lt 1).

14
TransDIF ETsum Density PYTHIA Tune A vs HERWIG
Leading Jet
Back-to-Back
transDIF is more sensitive to the hard
scattering component of the underlying event!
  • Use the leading jet to define the MAX and MIN
    transverse regions on an event-by-event basis
    with MAX (MIN) having the largest (smallest)
    charged PTsum density.
  • Shows the transDIF MAX-MIN ETsum density,
    dETsum/dhdf, for all particles (h lt 1) versus
    PT(jet1) for Leading Jet and Back-to-Back
    events.

15
Possible Scenario??
  • PYTHIA Tune A fits the charged particle PTsum
    density for pT gt 0.5 GeV/c, but it does not
    produce enough ETsum for towers with ET gt 0.1 GeV.
  • It is possible that there is a sharp rise in the
    number of particles in the underlying event at
    low pT (i.e. pT lt 0.5 GeV/c).
  • Perhaps there are two components, a vary soft
    beam-beam remnant component (Gaussian or
    exponential) and a hard multiple interaction
    component.

16
TransMAX/MIN ETsum Density PYTHIA Tune A vs
JIMMY
JIMMY was tuned to fit the energy density in the
transverse region for leading jet events!
JIMMY MPI J. M. Butterworth J. R. Forshaw M. H.
Seymour
Leading Jet
Back-to-Back
  • Shows the ETsum density, dETsum/dhdf, in the
    transMAX and transMIN region (all particles
    h lt 1) versus PT(jet1) for Leading Jet and
    Back-to-Back events.
  • Compares the (corrected) data with PYTHIA Tune A
    (with MPI) and a tuned version of JIMMY (with
    MPI, PTJIM 3.25 GeV/c) at the particle level.

17
TransMAX/MIN Nchg Density PYTHIA Tune A vs
JIMMY
Back-to-Back
Leading Jet
  • Shows the charged particle density, dNchg/dhdf,
    in the transMAX and transMIN region (pT gt 0.5
    GeV/c, h lt 1) versus PT(jet1) for Leading
    Jet and Back-to-Back events.
  • Compares the (corrected) data with PYTHIA Tune A
    (with MPI) and a tuned version of JIMMY (with
    MPI, PTJIM 3.25 GeV/c) at the particle level.

18
Transverse ltPTgt PYTHIA Tune A vs JIMMY
Back-to-Back
Leading Jet
  • Shows the charged particle ltPTgt in the
    transverse (pT gt 0.5 GeV/c, h lt 1) versus
    PT(jet1) for Leading Jet and Back-to-Back
    events.
  • Compares the (corrected) data with PYTHIA Tune A
    (with MPI) and HERWIG and a tuned version of
    JIMMY (with MPI, PTJIM 3.25 GeV/c) at the
    particle level.

Both JIMMY and HERWIG are too soft for pT gt 0.5
GeV/c!
19
CDF Run 1 PT(Z)
PYTHIA 6.2 CTEQ5L
UE Parameters
Parameter Tune A Tune A25 Tune A50
MSTP(81) 1 1 1
MSTP(82) 4 4 4
PARP(82) 2.0 GeV 2.0 GeV 2.0 GeV
PARP(83) 0.5 0.5 0.5
PARP(84) 0.4 0.4 0.4
PARP(85) 0.9 0.9 0.9
PARP(86) 0.95 0.95 0.95
PARP(89) 1.8 TeV 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25 0.25
PARP(67) 4.0 4.0 4.0
MSTP(91) 1 1 1
PARP(91) 1.0 2.5 5.0
PARP(93) 5.0 15.0 25.0
ISR Parameter
  • Shows the Run 1 Z-boson pT distribution (ltpT(Z)gt
    11.5 GeV/c) compared with PYTHIA Tune A
    (ltpT(Z)gt 9.7 GeV/c), Tune A25 (ltpT(Z)gt
    10.1 GeV/c), and Tune A50 (ltpT(Z)gt 11.2
    GeV/c).

Vary the intrensic KT!
Intrensic KT
20
CDF Run 1 PT(Z)
Tune used by the CDF-EWK group!
PYTHIA 6.2 CTEQ5L
Parameter Tune A Tune AW
MSTP(81) 1 1
MSTP(82) 4 4
PARP(82) 2.0 GeV 2.0 GeV
PARP(83) 0.5 0.5
PARP(84) 0.4 0.4
PARP(85) 0.9 0.9
PARP(86) 0.95 0.95
PARP(89) 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25
PARP(62) 1.0 1.25
PARP(64) 1.0 0.2
PARP(67) 4.0 4.0
MSTP(91) 1 1
PARP(91) 1.0 2.1
PARP(93) 5.0 15.0
UE Parameters
ISR Parameters
  • Shows the Run 1 Z-boson pT distribution (ltpT(Z)gt
    11.5 GeV/c) compared with PYTHIA Tune A
    (ltpT(Z)gt 9.7 GeV/c), and PYTHIA Tune AW
    (ltpT(Z)gt 11.7 GeV/c).

Effective Q cut-off, below which space-like
showers are not evolved.
Intrensic KT
The Q2 kT2 in as for space-like showers is
scaled by PARP(64)!
21
Jet-Jet Correlations (DØ)
  • MidPoint Cone Algorithm (R 0.7, fmerge 0.5)
  • L 150 pb-1 (Phys. Rev. Lett. 94 221801 (2005))
  • Data/NLO agreement good. Data/HERWIG agreement
    good.
  • Data/PYTHIA agreement good provided PARP(67)
    1.0?4.0 (i.e. like Tune A, best fit 2.5).

22
CDF Run 1 PT(Z)
PYTHIA 6.2 CTEQ5L
Parameter Tune DW Tune AW
MSTP(81) 1 1
MSTP(82) 4 4
PARP(82) 1.9 GeV 2.0 GeV
PARP(83) 0.5 0.5
PARP(84) 0.4 0.4
PARP(85) 1.0 0.9
PARP(86) 1.0 0.95
PARP(89) 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25
PARP(62) 1.25 1.25
PARP(64) 0.2 0.2
PARP(67) 2.5 4.0
MSTP(91) 1 1
PARP(91) 2.1 2.1
PARP(93) 15.0 15.0
UE Parameters
ISR Parameters
  • Shows the Run 1 Z-boson pT distribution (ltpT(Z)gt
    11.5 GeV/c) compared with PYTHIA Tune DW, and
    HERWIG.

Tune DW uses D0s perfered value of PARP(67)!
Intrensic KT
Tune DW has a lower value of PARP(67) and
slightly more MPI!
23
Transverse Nchg Density
PYTHIA 6.2 CTEQ5L
Three different amounts of MPI!
UE Parameters
Parameter Tune AW Tune DW Tune BW
MSTP(81) 1 1 1
MSTP(82) 4 4 4
PARP(82) 2.0 GeV 1.9 GeV 1.8 GeV
PARP(83) 0.5 0.5 0.5
PARP(84) 0.4 0.4 0.4
PARP(85) 0.9 1.0 1.0
PARP(86) 0.95 1.0 1.0
PARP(89) 1.8 TeV 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25 0.25
PARP(62) 1.25 1.25 1.25
PARP(64) 0.2 0.2 0.2
PARP(67) 4.0 2.5 1.0
MSTP(91) 1 1 1
PARP(91) 2.5 2.5 2/5
PARP(93) 15.0 15.0 15.0
ISR Parameter
  • Shows the transverse charged particle density,
    dN/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for PYTHIA Tune A, Tune AW,
    Tune DW, Tune BW, and HERWIG (without MPI).
  • Shows the transverse charged particle density,
    dN/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for Tune DW, ATLAS, and HERWIG
    (without MPI).

Three different amounts of ISR!
Intrensic KT
24
Transverse PTsum Density
PYTHIA 6.2 CTEQ5L
Three different amounts of MPI!
UE Parameters
Parameter Tune AW Tune DW Tune BW
MSTP(81) 1 1 1
MSTP(82) 4 4 4
PARP(82) 2.0 GeV 1.9 GeV 1.8 GeV
PARP(83) 0.5 0.5 0.5
PARP(84) 0.4 0.4 0.4
PARP(85) 0.9 1.0 1.0
PARP(86) 0.95 1.0 1.0
PARP(89) 1.8 TeV 1.8 TeV 1.8 TeV
PARP(90) 0.25 0.25 0.25
PARP(62) 1.25 1.25 1.25
PARP(64) 0.2 0.2 0.2
PARP(67) 4.0 2.5 1.0
MSTP(91) 1 1 1
PARP(91) 2.5 2.5 2/5
PARP(93) 15.0 15.0 15.0
ISR Parameter
  • Shows the transverse charged PTsum density,
    dPT/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for PYTHIA Tune A, Tune AW,
    Tune DW, Tune BW, and HERWIG (without MPI).
  • Shows the transverse charged PTsum density,
    dPT/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for Tune DW, ATLAS, and HERWIG
    (without MPI).

Three different amounts of ISR!
Intrensic KT
25
PYTHIA 6.2 Tunes
PYTHIA 6.2 CTEQ5L
s(MPI) at 1.96 TeV s(MPI) at 14 TeV
Tune A 309.7 mb 484.0 mb
Tune DW 351.7 mb 549.2 mb
Tune DWT 351.7 mb 829.1 mb
ATLAS 324.5 mb 768.0 mb
Parameter Tune A Tune DW Tune DWT ATLAS
MSTP(81) 1 1 1 1
MSTP(82) 4 4 4 4
PARP(82) 2.0 GeV 1.9 GeV 1.9409 GeV 1.8 GeV
PARP(83) 0.5 0.5 0.5 0.5
PARP(84) 0.4 0.4 0.4 0.5
PARP(85) 0.9 1.0 1.0 0.33
PARP(86) 0.95 1.0 1.0 0.66
PARP(89) 1.8 TeV 1.8 TeV 1.96 TeV 1.0 TeV
PARP(90) 0.25 0.25 0.16 0.16
PARP(62) 1.0 1.25 1.25 1.0
PARP(64) 1.0 0.2 0.2 1.0
PARP(67) 4.0 2.5 2.5 1.0
MSTP(91) 1 1 1 1
PARP(91) 1.0 2.1 2.1 1.0
PARP(93) 5.0 15.0 15.0 5.0
CDF Run 2 Data!
  • Shows the transverse charged particle density,
    dN/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for Tune A, DW, ATLAS, and
    HERWIG (without MPI).
  • Shows the transverse charged PTsum density,
    dPT/dhdf, versus PT(jet1) for leading jet
    events at 1.96 TeV for Tune A, DW, ATLAS, and
    HERWIG (without MPI).
  • Shows the transverse charged average pT, versus
    PT(jet1) for leading jet events at 1.96 TeV
    for Tune A, DW, ATLAS, and HERWIG (without MPI).

Identical to DW at 1.96 TeV but uses ATLAS
extrapolation to the LHC!
26
MIT Search Scheme 12
Exclusive 3 Jet Final State Challenge
CDF Data
At least 1 Jet (trigger jet) (PT gt 40 GeV/c,
h lt 1.0)
Normalized to 1
PYTHIA Tune A
Exactly 3 jets (PT gt 20 GeV/c, h lt 2.5)
R(j2,j3)
Order Jets by PT Jet1 highest PT, etc.
27
3Jexc R(j2,j3) Normalized
The data have more 3 jet events with small
R(j2,j3)!?
  • Let Ntrig40 equal the number of events with at
    least one jet with PT gt 40 geV and h lt 1.0
    (this is the offline trigger).
  • Let N3Jexc20 equal the number of events with
    exactly three jets with PT gt 20 GeV/c and h lt
    2.5 which also have at least one jet with PT gt 40
    GeV/c and h lt 1.0.

Normalized to N3JexcFr
  • Let N3JexcFr N3Jexc20/Ntrig40. The is the
    fraction of the offline trigger events that are
    exclusive 3-jet events.
  • The CDF data on dN/dR(j2,j3) at 1.96 TeV compared
    with PYTHIA Tune AW (PARP(67)4), Tune DW
    (PARP(67)2.5), Tune BW (PARP(67)1).
  • PARP(67) affects the initial-state radiation
    which contributes primarily to the region
    R(j2,j3) gt 1.0.

28
3Jexc R(j2,j3) Normalized
I do not understand the excess number of
events with R(j2,j3) lt 1.0. Perhaps this is
related to the soft energy problem?? For now
the best tune is PYTHIA Tune DW.
  • Let Ntrig40 equal the number of events with at
    least one jet with PT gt 40 geV and h lt 1.0
    (this is the offline trigger).
  • Let N3Jexc20 equal the number of events with
    exactly three jets with PT gt 20 GeV/c and h lt
    2.5 which also have at least one jet with PT gt 40
    GeV/c and h lt 1.0.

Normalized to N3JexcFr
  • Let N3JexcFr N3Jexc20/Ntrig40. The is the
    fraction of the offline trigger events that are
    exclusive 3-jet events.
  • The CDF data on dN/dR(j2,j3) at 1.96 TeV compared
    with PYTHIA Tune DW (PARP(67)2.5) and HERWIG
    (without MPI).
  • Final-State radiation contributes to the region
    R(j2,j3) lt 1.0.
  • If you ignore the normalization and normalize all
    the distributions to one then the data prefer
    Tune BW, but I believe this is misleading.

29
The Underlying Event in High PT Jet Production
(LHC)
Charged particle density versus PT(jet1)
The Underlying Event
Underlying event much more active at the LHC!
  • Charged particle density in the Transverse
    region versus PT(jet1) at 1.96 TeV for PY Tune
    AW and HERWIG (without MPI).
  • Charged particle density in the Transverse
    region versus PT(jet1) at 14 TeV for PY Tune AW
    and HERWIG (without MPI).

30
QCD Monte-Carlo Models Lepton-Pair Production
Underlying Event
  • Start with the perturbative Drell-Yan muon pair
    production and add initial-state gluon radiation
    (in the leading log approximation or modified
    leading log approximation).
  • The underlying event consists of the beam-beam
    remnants and from particles arising from soft or
    semi-soft multiple parton interactions (MPI).
  • Of course the outgoing colored partons fragment
    into hadron jet and inevitably underlying
    event observables receive contributions from
    initial and final-state radiation.

31
The Central Region in Drell-Yan Production
Look at the charged particle density and the
PTsum density in the central region!
Charged Particles (pT gt 0.5 GeV/c, h lt 1)
After removing the lepton-pair everything else is
the underlying event!
  • Look at the central region after removing the
    lepton-pair.
  • Study the charged particles (pT gt 0.5 GeV/c, h
    lt 1) and form the charged particle density,
    dNchg/dhdf, and the charged scalar pT sum
    density, dPTsum/dhdf, by dividing by the area in
    h-f space.

32
Drell-Yan Production (Run 2 vs LHC)
Lepton-Pair Transverse Momentum
ltpT(mm-)gt is much larger at the LHC!
Shapes of the pT(mm-) distribution at the
Z-boson mass.
Z
  • Average Lepton-Pair transverse momentum at the
    Tevatron and the LHC for PYTHIA Tune DW and
    HERWIG (without MPI).
  • Shape of the Lepton-Pair pT distribution at the
    Z-boson mass at the Tevatron and the LHC for
    PYTHIA Tune DW and HERWIG (without MPI).

33
The Underlying Event in Drell-Yan Production
The Underlying Event
Charged particle density versus M(pair)
HERWIG (without MPI) is much less active than PY
Tune AW (with MPI)!
Underlying event much more active at the LHC!
Z
Z
  • Charged particle density versus the lepton-pair
    invariant mass at 1.96 TeV for PYTHIA Tune AW and
    HERWIG (without MPI).
  • Charged particle density versus the lepton-pair
    invariant mass at 14 TeV for PYTHIA Tune AW and
    HERWIG (without MPI).

34
Extrapolations to the LHC Drell-Yan Production
Charged particle density versus M(pair)
The Underlying Event
Tune DW and DWT are identical at 1.96 TeV, but
have different MPI energy dependence!
Z
Z
  • Average charged particle density versus the
    lepton-pair invariant mass at 1.96 TeV for PYTHIA
    Tune A, Tune AW, Tune BW, Tune DW and HERWIG
    (without MPI).
  • Average charged particle density versus the
    lepton-pair invariant mass at 14 TeV for PYTHIA
    Tune DW, Tune DWT, ATLAS and HERWIG (without
    MPI).

35
Extrapolations to the LHC Drell-Yan Production
Charged particle charged PTsum density versus
M(pair)
The Underlying Event
The ATLAS tune has a much softer distribution
of charged particles than the CDF Run 2 Tunes!
Z
Z
  • Average charged PTsum density versus the
    lepton-pair invariant mass at 14 TeV for PYTHIA
    Tune DW, Tune DWT, ATLAS and HERWIG (without
    MPI).
  • Average charged PTsum density versus the
    lepton-pair invariant mass at 1.96 TeV for PYTHIA
    Tune A, Tune AW, Tune BW, Tune DW and HERWIG
    (without MPI).

36
Extrapolations to the LHC Drell-Yan Production
Charged particle density versus M(pair)
The Underlying Event
The ATLAS tune has a much softer distribution
of charged particles than the CDF Run 2 Tunes!
Charged Particles (hlt1.0, pT gt 0.5 GeV/c)
Charged Particles (hlt1.0, pT gt 0.9 GeV/c)
Z
Z
  • Average charged particle density (pT gt 0.5 GeV/c)
    versus the lepton-pair invariant mass at 14 TeV
    for PYTHIA Tune DW, Tune DWT, ATLAS and HERWIG
    (without MPI).
  • Average charged particle density (pT gt 0.9 GeV/c)
    versus the lepton-pair invariant mass at 14 TeV
    for PYTHIA Tune DW, Tune DWT, ATLAS and HERWIG
    (without MPI).

37
Proton-AntiProton Collisions at the Tevatron
The CDF Min-Bias trigger picks up most of the
hard core cross-section plus a small amount of
single double diffraction.
stot sEL sIN
stot sEL sSD sDD sHC
1.8 TeV 78mb 18mb 9mb
(4-7)mb (47-44)mb
CDF Min-Bias trigger 1 charged particle in
forward BBC AND 1 charged particle in backward BBC
The hard core component contains both hard
and soft collisions.
Beam-Beam Counters 3.2 lt h lt 5.9
38
CDF Min-Bias Data Charged Particle Density
ltdNchg/dhgt 4.2
ltdNchg/dhdfgt 0.67
  • Shows CDF Min-Bias data on the number of
    charged particles per unit pseudo-rapidity at 630
    and 1,800 GeV. There are about 4.2 charged
    particles per unit h in Min-Bias collisions at
    1.8 TeV (h lt 1, all PT).
  • Convert to charged particle density, dNchg/dhdf,
    by dividing by 2p. There are about 0.67 charged
    particles per unit h-f in Min-Bias collisions
    at 1.8 TeV (h lt 1, all PT).

39
CDF Min-Bias Data Energy Dependence
LHC?
  • Shows the center-of-mass energy dependence of the
    charged particle density, dNchg/dhdf, for
    Min-Bias collisions at h 0. Also show a log
    fit (Fit 1) and a (log)2 fit (Fit 2) to the CDF
    plus UA5 data.
  • What should we expect for the LHC?

40
PYTHIA Tune A Min-Bias Soft Hard
Tuned to fit the underlying event!
PYTHIA Tune A CDF Run 2 Default
12 of Min-Bias events have PT(hard) gt 5 GeV/c!
1 of Min-Bias events have PT(hard) gt 10 GeV/c!
  • PYTHIA regulates the perturbative 2-to-2
    parton-parton cross sections with cut-off
    parameters which allows one to run with PT(hard)
    gt 0. One can simulate both hard and soft
    collisions in one program.

Lots of hard scattering in Min-Bias!
  • The relative amount of hard versus soft
    depends on the cut-off and can be tuned.
  • This PYTHIA fit predicts that 12 of all
    Min-Bias events are a result of a hard 2-to-2
    parton-parton scattering with PT(hard) gt 5 GeV/c
    (1 with PT(hard) gt 10 GeV/c)!

41
PYTHIA Tune A LHC Min-Bias Predictions
LHC?
  • Shows the center-of-mass energy dependence of the
    charged particle density, dNchg/dhdf, for
    Min-Bias collisions compared with PYTHIA Tune A
    with PT(hard) gt 0.
  • PYTHIA was tuned to fit the underlying event in
    hard-scattering processes at 1.8 TeV and 630 GeV.
  • PYTHIA Tune A predicts a 42 rise in dNchg/dhdf
    at h 0 in going from the Tevatron (1.8 TeV) to
    the LHC (14 TeV). Similar to HERWIG soft
    min-bias, 4 charged particles per unit h becomes
    6.

42
PYTHIA Tune A LHC Min-Bias Predictions
12 of Min-Bias events have PT(hard) gt 10 GeV/c!
LHC?
  • Shows the center-of-mass energy dependence of the
    charged particle density, dNchg/dhdfdPT, for
    Min-Bias collisions compared with PYTHIA Tune A
    with PT(hard) gt 0.

1 of Min-Bias events have PT(hard) gt 10 GeV/c!
  • PYTHIA Tune A predicts that 1 of all Min-Bias
    events at 1.8 TeV are a result of a hard 2-to-2
    parton-parton scattering with PT(hard) gt 10 GeV/c
    which increases to 12 at 14 TeV!

43
PYTHIA 6.2 Tunes LHC Min-Bias Predictions
  • Shows the predictions of PYTHIA Tune A, Tune DW,
    Tune DWT, and the ATLAS tune for the charged
    particle density dN/dh and dN/dY at 14 TeV (all
    pT).
  • PYTHIA Tune A and Tune DW predict about 6 charged
    particles per unit h at h 0, while the ATLAS
    tune predicts around 9.
  • PYTHIA Tune DWT is identical to Tune DW at 1.96
    TeV, but extrapolates to the LHC using the ATLAS
    energy dependence.

44
PYTHIA 6.2 Tunes LHC Min-Bias Predictions
  • Shows the predictions of PYTHIA Tune A, Tune DW,
    Tune DWT, and the ATLAS tune for the charged
    particle pT distribution at 14 TeV (h lt 1) and
    the average number of charged particles with pT gt
    pTmin (h lt 1).
  • The ATLAS tune has many more soft particles
    than does any of the CDF Tunes. The ATLAS tune
    has ltpTgt 548 MeV/c while Tune A has ltpTgt 641
    MeV/c (100 MeV/c more per particle)!

45
Summary
More work needs to be done in comparing the
various tunes at the LHC. The ATLAS tune cannot
be right because it does not fit the
Tevatron data. Right now I like Tune
DW. Probably no tune will fit the LHC data. That
is why we plan to measure MBUE at CMS and retune
the Monte-Carlo models!
  • PYTHIA Tune A does not produce enough soft
    energy in the underlying event! JIMMY 325
    (PTJIM 3.25 GeV/c) fits the energy in the
    underlying event but does so by producing too
    many particles (i.e. it is too soft).
  • The ATLAS tune is goofy! It produces too many
    soft particles. The charged particle ltpTgt is
    too low and does not agree with the CDF Run 2
    data. The ATLAS tune agrees with ltNchggt but not
    with ltPTsumgt at the Tevatron.
  • PYTHIA Tune DW is very similar to Tune A except
    that it fits the CDF PT(Z) distribution and it
    uses the DØ prefered value of PARP(67) 2.5
    (determined from the dijet Df distribution).
  • PYTHIA Tune DWT is identical to Tune DW at 1.96
    TeV but uses the ATLAS energy extrapolation to
    the LHC (i.e. PARP(90) 0.16).

46
Conclusions
I think more work needs to be done in comparing
the various tunes. The ATLAS tune cannot be
right because it does not fit the Tevatron data.
Right now I like Tune DW. Probably no tune will
fit the CMS data. That is why we want to measure
MBUE at CMS and retune the Monte-Carlo models!
Tevatron LHC
  • We cannot use the new underlying event model in
    PYTHIA 6.3. It has not been studied (and tuned)
    well enough yet!
  • The ATLAS tune is goofy! It produces too many
    soft particles. The charged particle ltpTgt is
    too low and does not agree with the CDF Run 2
    data. The ATLAS tune agrees with ltNchggt but not
    with ltPTsumgt at the Tevatron.
  • PYTHIA Tune DW is very similar to Tune A except
    that it fits the CDF PT(Z) distribution and it
    uses the DØ prefered value of PARP(67) 2.5
    (determined from the dijet Df distribution).
  • PYTHIA Tune DWT is identical to Tune DW at 1.96
    TeV but uses the ATLAS energy extrapolation to
    the LHC (i.e. PARP(90) 0.16).
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