YETI

1
YETI11 The Standard Model at the Energy
Frontier
Min-Bias and the Underlying Event at the LHC
Rick Field University of Florida
1st Lecture

• What is the underlying event and how is it
  related to min-bias collisions.

CMS

• Lessons learned about min-bias and the
  underlying event at the TEVATRON.

ATLAS

• Predicting the behavior of the underlying event
  at the LHC. What we expected to see.

UEMB_at_CMS
2
QCD Monte-Carlo ModelsHigh 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.

3
QCD Monte-Carlo ModelsLepton-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-state radiation.

4
MPI, Pile-Up, and Overlap
MPI Multiple Parton Interactions

• MPI Additional 2-to-2 parton-parton scatterings
  within a single hadron-hadron collision.

Pile-Up
Interaction Region Dz

• Pile-Up More than one hadron-hadron collision in
  the beam crossing.

Overlap

• Overlap An experimental timing issue where a
  hadron-hadron collision from the next beam
  crossing gets included in the hadron-hadron
  collision from the current beam crossing because
  the next crossing happened before the event could
  be read out.

5
Proton-Proton Collisions
stot sEL sSD sDD sHC
stot sEL sIN
ND
Inelastic Non-Diffractive Component
The hard core component contains both hard
and soft collisions.
6
The Inelastic Non-Diffractive Cross-Section
Occasionally one of the parton-parton collisions
is hard (pT gt 2 GeV/c)
Majority of min-bias events!
Semi-hard parton-parton collision (pT lt 2
GeV/c)




Multiple-parton interactions (MPI)!
7
The Underlying Event
Select inelastic non-diffractive events that
contain a hard scattering
1/(pT)4? 1/(pT2pT02)2
Hard parton-parton collisions is hard (pT gt 2
GeV/c)
Semi-hard parton-parton collision (pT lt 2
GeV/c)
The underlying-event (UE)!



Given that you have one hard scattering it is
more probable to have MPI! Hence, the UE has
more activity than min-bias.
Multiple-parton interactions (MPI)!
8
Model of sND
Allow leading hard scattering to go to zero pT
with same cut-off as the MPI!
Semi-hard parton-parton collision (pT lt 2
GeV/c)
1/(pT)4? 1/(pT2pT02)2
Model of the inelastic non-diffractive cross
section!




Multiple-parton interactions (MPI)!
9
UE Tunes
Allow primary hard-scattering to go to pT 0
with same cut-off!
Underlying Event
All of Ricks tunes (except X2) A, AW, AWT,DW,
DWT, D6, D6T, CW, X1, and Tune Z1, are UE tunes!
Fit the underlying event in a hard scattering
process.
1/(pT)4? 1/(pT2pT02)2
Min-Bias (ND)
Min-Bias (add single double diffraction)



Predict MB (ND)!

Predict MB (IN)!
10
MB Tunes
Underlying Event
Most of Peter Skands tunes S320 Perugia 0, S325
Perugia X, S326 Perugia 6 are MB tunes!
Predict the underlying event in a hard
scattering process!
Min-Bias (ND)



Fit MB (ND).

11
MBUE Tunes
Underlying Event
Most of Hendriks Professor tunes ProQ20,
P329 are MBUE!
Fit the underlying event in a hard scattering
process!
Min-Bias (ND)
Simultaneous fit to both MB UE



The ATLAS AMBT1 Tune is an MBUE tune,
but because they include in the fit the ATLAS UE
data with PTmax gt 10 GeV/c (big errors) the LHC
UE data does not have much pull (hence mostly an
MB tune!).
Fit MB (ND).

12
Traditional Approach
CDF Run 1 Analysis
Charged Particle Df Correlations PT gt PTmin h lt
hcut
Leading Calorimeter Jet or Leading Charged
Particle Jet or Leading Charged Particle
or Z-Boson
Transverse region very sensitive to the
underlying event!

• Look at charged particle correlations in the
  azimuthal angle Df relative to a leading object
  (i.e. CaloJet1, ChgJet1, PTmax, Z-boson). For
  CDF PTmin 0.5 GeV/c hcut 1.

• 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 area in h-f
  space, DhDf 2hcut120o 2hcut2p/3. Construct
  densities by dividing by the area in h-f space.

13
ISAJET 7.32 (without MPI)Transverse Density
ISAJET uses a naïve leading-log parton
shower-model which does not agree with the data!
ISAJET
Hard Component
Beam-Beam Remnants

• Plot shows average transverse charge particle
  density (hlt1, pTgt0.5 GeV) versus PT(charged
  jet1) compared to the QCD hard scattering
  predictions of ISAJET 7.32 (default parameters
  with PT(hard)gt3 GeV/c) .
• The predictions of ISAJET are divided into two
  categories charged particles that arise from the
  break-up of the beam and target (beam-beam
  remnants) and charged particles that arise from
  the outgoing jet plus initial and final-state
  radiation (hard scattering component).

14
HERWIG 6.4 (without MPI)Transverse Density
HERWIG uses a modified leading-log parton
shower-model which does agrees better with the
data!
HERWIG
Hard Component
Beam-Beam Remnants

• Plot shows average transverse charge particle
  density (hlt1, pTgt0.5 GeV) versus PT(charged
  jet1) compared to the QCD hard scattering
  predictions of HERWIG 5.9 (default parameters
  with PT(hard)gt3 GeV/c without MPI).
• The predictions of HERWIG are divided into two
  categories charged particles that arise from the
  break-up of the beam and target (beam-beam
  remnants) and charged particles that arise from
  the outgoing jet plus initial and final-state
  radiation (hard scattering component).

15
HERWIG 6.4 (without MPI)Transverse PT
Distribution
HERWIG has the too steep of a pT dependence of
the beam-beam remnant component of the
underlying event!
Herwig PT(chgjet1) gt 30 GeV/c Transverse
ltdNchg/dhdfgt 0.51
Herwig PT(chgjet1) gt 5 GeV/c ltdNchg/dhdfgt 0.40

• Compares the average transverse charge particle
  density (hlt1, pTgt0.5 GeV) versus PT(charged
  jet1) and the pT distribution of the
  transverse density, dNchg/dhdfdPT with the QCD
  hard scattering predictions of HERWIG 6.4
  (default parameters with PT(hard)gt3 GeV/c without
  MPI). Shows how the transverse charge particle
  density is distributed in pT.

16
MPI Multiple PartonInteractions

• PYTHIA models the soft component of the
  underlying event with color string fragmentation,
  but in addition includes a contribution arising
  from multiple parton interactions (MPI) in which
  one interaction is hard and the other is
  semi-hard.

• The probability that a hard scattering events
  also contains a semi-hard multiple parton
  interaction can be varied but adjusting the
  cut-off for the MPI.
• One can also adjust whether the probability of a
  MPI depends on the PT of the hard scattering,
  PT(hard) (constant cross section or varying with
  impact parameter).
• One can adjust the color connections and flavor
  of the MPI (singlet or nearest neighbor, q-qbar
  or glue-glue).
• Also, one can adjust how the probability of a MPI
  depends on PT(hard) (single or double Gaussian
  matter distribution).

17
Tuning PYTHIA 6.2Multiple Parton Interaction
Parameters
Parameter Default Description
PARP(83) 0.5 Double-Gaussian Fraction of total hadronic matter within PARP(84)
PARP(84) 0.2 Double-Gaussian Fraction of the overall hadron radius containing the fraction PARP(83) of the total hadronic matter.
PARP(85) 0.33 Probability that the MPI produces two gluons with color connections to the nearest neighbors.
PARP(86) 0.66 Probability that the MPI produces two gluons either as described by PARP(85) or as a closed gluon loop. The remaining fraction consists of quark-antiquark pairs.
PARP(89) 1 TeV Determines the reference energy E0.
PARP(82) 1.9 GeV/c The cut-off PT0 that regulates the 2-to-2 scattering divergence 1/PT4?1/(PT2PT02)2
PARP(90) 0.16 Determines the energy dependence of the cut-off PT0 as follows PT0(Ecm) PT0(Ecm/E0)e with e PARP(90)
PARP(67) 1.0 A scale factor that determines the maximum parton virtuality for space-like showers. The larger the value of PARP(67) the more initial-state radiation.
Hard Core
Determines the energy dependence of the MPI!
Determine by comparing with 630 GeV data!
Affects the amount of initial-state radiation!
Take E0 1.8 TeV
Reference point at 1.8 TeV
18
PYTHIA 6.206 Defaults
MPI constant probability scattering
PYTHIA default parameters
Parameter 6.115 6.125 6.158 6.206
MSTP(81) 1 1 1 1
MSTP(82) 1 1 1 1
PARP(81) 1.4 1.9 1.9 1.9
PARP(82) 1.55 2.1 2.1 1.9
PARP(89) 1,000 1,000 1,000
PARP(90) 0.16 0.16 0.16
PARP(67) 4.0 4.0 1.0 1.0

• Plot shows the Transverse charged particle
  density versus PT(chgjet1) compared to the QCD
  hard scattering predictions of PYTHIA 6.206
  (PT(hard) gt 0) using the default parameters for
  multiple parton interactions and CTEQ3L, CTEQ4L,
  and CTEQ5L.

Default parameters give very poor description of
the underlying event!
Note Change PARP(67) 4.0 (lt 6.138) PARP(67)
1.0 (gt 6.138)
19
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)
20
Transverse Conesvs Transverse Regions
Cone Analysis (Tano, Kovacs, Huston, Bhatti)
Transverse Cone p(0.7)20.49p
Transverse Region 2p/30.67p

• Sum the PT of charged particles in two cones of
  radius 0.7 at the same h as the leading jet but
  with DF 90o.
• Plot the cone with the maximum and minimum PTsum
  versus the ET of the leading (calorimeter) jet.

21
Energy Dependenceof the Underlying Event
Cone Analysis (Tano, Kovacs, Huston, Bhatti)
630 GeV
1,800 GeV
PYTHIA 6.115 PT0 1.4 GeV
PYTHIA 6.115 PT0 2.0 GeV

• Sum the PT of charged particles (pT gt 0.4 GeV/c)
  in two cones of radius 0.7 at the same h as the
  leading jet but with DF 90o. Plot the cone
  with the maximum and minimum PTsum versus the ET
  of the leading (calorimeter) jet.
• Note that PYTHIA 6.115 is tuned at 630 GeV with
  PT0 1.4 GeV and at 1,800 GeV with PT0 2.0
  GeV. This implies that e PARP(90) should be
  around 0.30 instead of the 0.16 (default).
• For the MIN cone 0.25 GeV/c in radius R 0.7
  implies a PTsum density of dPTsum/dhdf 0.16
  GeV/c and 1.4 GeV/c in the MAX cone implies
  dPTsum/dhdf 0.91 GeV/c (average PTsum density
  of 0.54 GeV/c per unit h-f).

22
Transverse Charged DensitiesEnergy Dependence
Increasing e produces less energy dependence for
the UE resulting in less UE activity at the LHC!
Lowering PT0 at 630 GeV (i.e. increasing e)
increases UE activity resulting in less energy
dependence.

• Shows the transverse charged PTsum density
  (hlt1, PTgt0.4 GeV) versus PT(charged jet1) at
  630 GeV predicted by HERWIG 6.4 (PT(hard) gt 3
  GeV/c, CTEQ5L) and a tuned version of PYTHIA
  6.206 (PT(hard) gt 0, CTEQ5L, Set A, e 0, e
  0.16 (default) and e 0.25 (preferred)).
• Also shown are the PTsum densities (0.16 GeV/c
  and 0.54 GeV/c) determined from the Tano, Kovacs,
  Huston, and Bhatti transverse cone analysis at
  630 GeV.

Rick Field Fermilab MC Workshop October 4, 2002!
Reference point E0 1.8 TeV
23
Run 1 vs Run 2 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).

24
Run 1 vs Run 2 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).

25
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.

26
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.

27
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.

28
Rick Field University of ChicagoJuly 11, 2006
Back-to-Back
Leading Jet

• 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).

29
Rick Field University of ChicagoJuly 11, 2006
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).

30
Rick Field University of ChicagoJuly 11, 2006
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.

31
Rick Field University of ChicagoJuly 11, 2006
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.

32
Charged Particle Multiplicity
Tune A!
No MPI!

• Data at 1.96 TeV on the charged particle
  multiplicity (pT gt 0.4 GeV/c, h lt 1) for
  min-bias collisions at CDF Run 2
  (non-diffractive cross-section).

• The data are compared with PYTHIA Tune A and Tune
  A without multiple parton interactions
  (pyAnoMPI).

33
PYTHIA Tune A Min-BiasSoft Hard
New
Ten decades!
12 of Min-Bias events have PT(hard) gt 5 GeV/c!
1 of Min-Bias events have PT(hard) gt 10 GeV/c!
Lots of hard scattering in Min-Bias at the
Tevatron!

• Comparison of PYTHIA Tune A with the pT
  distribution of charged particles for min-bias
  collisions at CDF Run 1 (non-diffractive
  cross-section).

pT 50 GeV/c!

• PYTHIA Tune A 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)!

34
CDF Charged pT Distribution
Erratum November 18, 2010
Excess events at large pT!
No excess at large pT!
50 GeV/c!

• Published CDF data on the pT distribution of
  charged particles in Min-Bias collisions (ND) at
  1.96 TeV compared with PYTHIA Tune A.

CDF inconsistent with CMS and UA1!
CDF consistent with CMS and UA1!
35
Min-Bias Correlations

• Data at 1.96 TeV on the average pT of charged
  particles versus the number of charged particles
  (pT gt 0.4 GeV/c, h lt 1) for min-bias
  collisions at CDF Run 2. The data are corrected
  to the particle level and are compared with
  PYTHIA Tune A at the particle level (i.e.
  generator level).

36
Min-Bias Average PT versus Nchg

• Beam-beam remnants (i.e. soft hard core) produces
  low multiplicity and small ltpTgt with ltpTgt
  independent of the multiplicity.

• Hard scattering (with no MPI) produces large
  multiplicity and large ltpTgt.

• Hard scattering (with MPI) produces large
  multiplicity and medium ltpTgt.

This observable is sensitive to the MPI tuning!



The CDF min-bias trigger picks up most of the
hard core component!
37
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)!
38
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).

39
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!
40
PYTHIA 6.2 Tunes
All use LO as with L 192 MeV!
Parameter Tune AW Tune DW Tune D6
PDF CTEQ5L CTEQ5L CTEQ6L
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 2.5
MSTP(91) 1 1 1
PARP(91) 2.1 2.1 2.1
PARP(93) 15.0 15.0 15.0
UE Parameters
Uses CTEQ6L
Tune A energy dependence! (not the default)
ISR Parameter
Intrinsic KT
41
PYTHIA 6.2 Tunes
These are all old PYTHIA 6.2 Tunes! There are now
many PYTHIA 6.4 tunes (S320 Perugia 0, Tune
Z1) and some PYTHIA 8 tunes (Tune 1, Hendrik
tunes).
All use LO as with L 192 MeV!
Parameter Tune DWT Tune D6T ATLAS
PDF CTEQ5L CTEQ6L CTEQ5L
MSTP(81) 1 1 1
MSTP(82) 4 4 4
PARP(82) 1.9409 GeV 1.8387 GeV 1.8 GeV
PARP(83) 0.5 0.5 0.5
PARP(84) 0.4 0.4 0.5
PARP(85) 1.0 1.0 0.33
PARP(86) 1.0 1.0 0.66
PARP(89) 1.96 TeV 1.96 TeV 1.0 TeV
PARP(90) 0.16 0.16 0.16
PARP(62) 1.25 1.25 1.0
PARP(64) 0.2 0.2 1.0
PARP(67) 2.5 2.5 1.0
MSTP(91) 1 1 1
PARP(91) 2.1 2.1 1.0
PARP(93) 15.0 15.0 5.0
UE Parameters
Tune B
Tune AW
Tune A
Old ATLAS energy dependence! (PYTHIA default)
Tune BW
ISR Parameter
Tune DW
Tune D6
Tune D
Tune D6T
Intrinsic KT
42
Min-Bias AssociatedCharged Particle Density
35 more at RHIC means 26 less at the LHC!
1.35
1.35
0.2 TeV ? 14 TeV (factor of 70 increase)
RHIC
LHC

• Shows the associated charged particle density
  in the transverse regions as a function of
  PTmax for charged particles (pT gt 0.5 GeV/c, h
  lt 1, not including PTmax) for min-bias events
  at 0.2 TeV and 14 TeV from PYTHIA Tune DW and
  Tune DWT at the particle level (i.e. generator
  level). The STAR data from RHIC favors Tune DW!

43
Min-Bias AssociatedCharged Particle Density
1.9
2.7
0.2 TeV ? 1.96 TeV (UE increase 2.7 times)
1.96 TeV ? 14 TeV (UE increase 1.9 times)
RHIC
LHC
Tevatron

• Shows the associated charged particle density
  in the transverse region as a function of PTmax
  for charged particles (pT gt 0.5 GeV/c, h lt 1,
  not including PTmax) for min-bias events at 0.2
  TeV, 1.96 TeV and 14 TeV predicted by PYTHIA Tune
  DW at the particle level (i.e. generator level).

44
The Underlying Event at STAR

• At STAR they have measured the underlying event
  at W 200 GeV (h lt 1, pT gt 0.2 GeV) and
  compared their uncorrected data with PYTHIA Tune
  A STAR-SIM.

45
Min-Bias AssociatedCharged Particle Density
If the LHC data are not in the range shown here
then we learn new (QCD) physics! Rick Field
October 13, 2009
RDF LHC Prediction!
Tevatron
LHC

• Shows the associated charged particle density
  in the transverse region as a function of PTmax
  for charged particles (pT gt 0.5 GeV/c, h lt 1,
  not including PTmax) for min-bias events at
  1.96 TeV from PYTHIA Tune A, Tune S320, Tune
  N324, and Tune P329 at the particle level (i.e.
  generator level).

• Extrapolations of PYTHIA Tune A, Tune DW, Tune
  DWT, Tune S320, Tune P329, and pyATLAS to the LHC.

46
Transverse Charged Density

• Shows the charged particle density in the
  transverse region for charged particles (pT gt
  0.5 GeV/c, h lt 1) at 7 TeV as defined by PTmax,
  PT(chgjet1), and PT(muon-pair) from PYTHIA Tune
  DW at the particle level (i.e. generator level).
  Charged particle jets are constructed using the
  Anti-KT algorithm with d 0.5.

47
Min-Bias AssociatedCharged Particle Density
LHC14
LHC10
LHC7
Tevatron
900 GeV
RHIC
0.2 TeV ? 1.96 TeV (UE increase 2.7 times)
1.96 TeV ? 14 TeV (UE increase 1.9 times)
RHIC
LHC
Tevatron

• Shows the associated charged particle density
  in the transverse region as a function of PTmax
  for charged particles (pT gt 0.5 GeV/c, h lt 1,
  not including PTmax) for min-bias events at 0.2
  TeV, 0.9 TeV, 1.96 TeV, 7 TeV, 10 TeV, 14 TeV
  predicted by PYTHIA Tune DW at the particle level
  (i.e. generator level).

Linear scale!
48
Min-Bias AssociatedCharged Particle Density
LHC14
LHC10
LHC7
Tevatron
900 GeV
RHIC
7 TeV ? 14 TeV (UE increase 20)
LHC7
LHC14
Linear on a log plot!

• Shows the associated charged particle density
  in the transverse region as a function of PTmax
  for charged particles (pT gt 0.5 GeV/c, h lt 1,
  not including PTmax) for min-bias events at 0.2
  TeV, 0.9 TeV, 1.96 TeV, 7 TeV, 10 TeV, 14 TeV
  predicted by PYTHIA Tune DW at the particle level
  (i.e. generator level).

Log scale!
49
Conclusions November 2009

• We are making good progress in understanding and
  modeling the underlying event. RHIC data at
  200 GeV are very important!

• The new Pythia pT ordered tunes (py64 S320 and
  py64 P329) are very similar to Tune A, Tune AW,
  and Tune DW. At present the new tunes do not fit
  the data better than Tune AW and Tune DW.
  However, the new tune are theoretically
  preferred!

• It is clear now that the default value PARP(90)
  0.16 is not correct and the value should be
  closer to the Tune A value of 0.25.

• The new and old PYTHIA tunes are beginning to
  converge and I believe we are finally in a
  position to make some legitimate predictions at
  the LHC!

• All tunes with the default value PARP(90) 0.16
  are wrong and are overestimating the activity of
  min-bias and the underlying event at the LHC!
  This includes all my T tunes and the (old)
  ATLAS tunes!

UEMB_at_CMS

• Need to measure Min-Bias and the underlying
  event at the LHC as soon as possible to see if
  there is new QCD physics to be learned!

50
Transverse Charged Particle Density
Leading Charged Particle Jet, chgjet1.
Prediction!
Leading Charged Particle, PTmax.

• Fake data (from MC) at 900 GeV on the
  transverse charged particle density, dN/dhdf,
  as defined by the leading charged particle
  (PTmax) and the leading charged particle jet
  (chgjet1) for charged particles with pT gt 0.5
  GeV/c and h lt 2. The fake data (from PYTHIA
  Tune DW) are generated at the particle level
  (i.e. generator level) assuming 0.5 M min-bias
  events at 900 GeV (361,595 events in the plot).

Rick Field MBUE_at_CMS Workshop CERN, November 6,
2009
51
Transverse Charge Density
Rick Field MBUE_at_CMS Workshop CERN, November 6,
2009
factor of 2!
Prediction!
900 GeV ? 7 TeV (UE increase factor of 2)
LHC 900 GeV
LHC 7 TeV
0.4 ? 0.8

• Shows the charged particle density in the
  transverse region for charged particles (pT gt
  0.5 GeV/c, h lt 2) at 900 GeV and 7 TeV as
  defined by PTmax from PYTHIA Tune DW and at the
  particle level (i.e. generator level).

52
YETI11 The Standard Model at the Energy
Frontier
Min-Bias and the Underlying Event at the LHC
Rick Field University of Florida
2nd Lecture

• How well did we do at predicting the behavior of
  the underlying event at the LHC (900 GeV and 7
  TeV)?

CMS
In lecture 2 we will examine how well we did at
predicting the underlying event and min-bias
at the LHC and look at some new tunes that came
after seeing the LHC data.

• How well did we do at predicting the behavior of
  min-bias collisions at the LHC (900 GeV and 7
  TeV)?

ATLAS

• PYTHIA 6.4 Tune Z1 New CMS 6.4 tune (pT-ordered
  parton showers and new MPI).

• New Physics in Min-Bias?? Observation of
  long-range same-side correlations at 7 TeV.

• Strange particle production A problem for the
  models?

UEMB_at_CMS