The Underlying Event in Hard Scattering Processes

1
The Underlying Event inHard Scattering Processes
The Underlying Event beam-beam
remnants initial-state radiation multiple-parton
interactions

• The underlying event in a hard scattering process
  is a complicated and not very well understood
  object. It is an interesting region since it
  probes the interface between perturbative and
  non-perturbative physics.
• There are two CDF analyses which quantitatively
  study the underlying event and compare with the
  QCD Monte-Carlo models.
• It is important to model this region well since
  it is an unavoidable background to all collider
  observables. Also, we need a good model of
  min-bias (zero-bias) collisions.

CDF WYSIWYGDf Rick Field David Stuart Rich Haas
CDF QFLCones Valeria Tano Eve Kovacs Joey
Huston Anwar Bhatti
Ph.D. Thesis
Ph.D. Thesis
Different but related problem!
2
Beam-Beam Remnants

• The underlying event in a hard scattering process
  has a hard component (particles that arise from
  initial final-state radiation and from the
  outgoing hard scattered partons) and a soft
  component (beam-beam remnants).
• However the soft component is color connected
  to the hard component so this separation is (at
  best) an approximation.

Min-Bias?

• For ISAJET (no color flow) the soft and hard
  components are completely independent and the
  model for the beam-beam remnant component is the
  same as for min-bias (cut pomeron) but with a
  larger ltPTgt.
• HERWIG breaks the color connection with a soft
  q-qbar pair and then models the beam-beam remnant
  component the same as HERWIG min-bias (cluster
  decay).

3
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).

4
WYSIWYG Comparing Datawith QCD Monte-Carlo
Models
Charged Particle Data
QCD Monte-Carlo
WYSIWYG What you see is what you get. Almost!
Select clean region
Make efficiency corrections
Look only at the charged particles measured by
the CTC.

• Zero or one vertex
• zc-zv lt 2 cm, CTC d0 lt 1 cm
• Require PT gt 0.5 GeV, h lt 1
• Assume a uniform track finding efficiency of 92
• Errors include both statistical and correlated
  systematic uncertainties

• Require PT gt 0.5 GeV, h lt 1
• Make an 8 correction for the track finding
  efficiency
• Errors (statistical plus systematic) of around 5

compare
Small Corrections!
Corrected theory
Uncorrected data
5
Define Charged Particle Jetsas Circular
Regions in h-f Space
Jets contain particles from the underlying
event in addition to particles from the outgoing
partons.

• Order Charged Particles in PT (h lt 1 PT gt 0.5
  GeV/c).
• Start with highest PT particle and include in the
  jet all particles (h lt 1 PT gt 0.5 GeV/c)
  within radius R 0.7 (considering each particle
  in decreasing PT and recalculating the centroid
  of the jet after each new particle is added to
  the jet).
• Go to next highest PT particle (not already
  included in a previous jet) and include in the
  jet all particles (h lt 1 PT gt 0.5 GeV/c)
  within radius R 0.7 (not already included in a
  previous jet).
• Continue until all particles are in a jet.
• Maximum number of jets is about
  2(2)(2p)/(p(0.7)2) or 16.

6 particles 5 jets
6
The Evolution of Charged Jetsfrom 0.5 GeV/c to
50 GeV/c
QCD hard scattering predictions of Herwig 5.9,
Isajet 7.32, and Pythia 6.115
JET20 data connects on smoothly to the Min-Bias
data
Local Jet Observable

• Compares data on the average number of charged
  particles within charged Jet1 (leading jet, R
  0.7) with the QCD hard scattering predictions of
  HERWIG 5.9, ISAJET 7.32, and PYTHIA 6.115
  (default parameters with PT(hard)gt3 GeV/c)..
• Only charged particles with h lt 1 and PT gt 0.5
  GeV/c are included and the theory has been
  corrected for track finding efficiency.

7
Jet1 Size vs PT(chgjet1)
JET20 data connects on smoothly to the Min-Bias
data
Isajet 7.32
Pythia 6.115
Herwig 5.9
Local Jet Observable

• Compares data on the average radius containing
  80 of the particles and 80 of the PT of
  chgjet1 (leading jet) with the QCD hard
  scattering predictions of HERWIG 5.9, ISAJET
  7.32, and PYTHIA 6.115 (default parameters with
  PT(hard)gt3 GeV/c).
• Only charged particles with h lt 1 and PT gt 0.5
  GeV/c are included and the theory has been
  corrected for track finding efficiency.

8
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.

9
Charged Multiplicity versus PT(chgjet1)
Underlying Event plateau

• Data on the average number of toward
  (Dflt60o), transverse (60ltDflt120o), and
  away (Dfgt120o) charged particles (PT gt 0.5
  GeV, h lt 1, including jet1) as a function of
  the transverse momentum of the leading charged
  particle jet. Each point corresponds to the
  ltNchggt in a 1 GeV bin. The solid (open) points
  are the Min-Bias (JET20) data. The errors on the
  (uncorrected) data include both statistical and
  correlated systematic uncertainties.

10
Transverse PT Distribution
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
2.3
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
2.2

• Comparison of the transverse ltNchggt versus
  PT(charged jet1) with the PT distribution of the
  transverse ltNchggt, dNchg/dPT. The integral of
  dNchg/dPT is the transverse ltNchggt. Shows how
  the transverse ltNchggt is distributed in PT.

11
Max/Min Transverse Nchg versus PT(chgjet1)
Area DhDf 2x60o 2p/3
TransMAX
TransMIN

• Define TransMAX and TransMIN to be the
  maximum and minimum of the region 60oltDflt120o
  (60olt-Dflt120o) on an event by event basis. The
  overall transverse region is the sum of
  TransMAX and TransMIN. The plot shows the
  average TransMAX Nchg and TransMIN Nchg
  versus PT(charged jet1).
• The solid (open) points are the Min-Bias (JET20)
  data. The errors on the (uncorrected) data
  include both statistical and correlated
  systematic uncertainties.

12
QFL Comparing Datawith QCD Monte-Carlo Models
Charged Particle And Calorimeter Data
QCD Monte-Carlo
Look only at both the charged particles measured
by the CTC and the calorimeter data.
QFL detector simulation
Select region
Tano-Kovacs-Huston-Bhatti

• Calorimeter tower threshold 50 MeV, Etot lt
  1800 GeV, hlj lt 0.7, zvtx lt 60 cm, 1 and only
  1 class 10, 11, or 12 vertex
• Tracks zc-zv lt 5 cm, CTC d0 lt 0.5 cm, PT gt
  0.4 GeV, h lt 1, correct for track finding
  efficiency

compare

• Require PT gt 0.4 GeV, h lt 1

13
Transverse Cones
Tano-Kovacs-Huston-Bhatti
Transverse Cone p(0.7)20.49p
1.36
Transverse Region 2p/30.67p

• Sum the PT of charged particles (or the energy)
  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..

14
Transverse Regionsvs Transverse Cones
Field-Stuart-Haas
2.9 GeV/c
2.1 GeV/c
0.5 GeV/c
0 lt PT(chgjet1) lt 50 GeV/c
0.4 GeV/c

• Multiply by ratio of the areas Max(2.1
  GeV/c)(1.36) 2.9 GeV/c Min(0.4 GeV/c)(1.36)
  0.5 GeV/c.
• This comparison is only qualitative!

50 lt ET(jet1) lt 300 GeV/c
Tano-Kovacs-Huston-Bhatti
Can study the underlying event over a wide
range!
15
Transverse Nchg versus PT(chgjet1)
Isajet 7.32
Pythia 6.115
Herwig 5.9

• Plot shows the transverse ltNchggt versus
  PT(chgjet1) compared to the the QCD hard
  scattering predictions of HERWIG 5.9, ISAJET
  7.32, and PYTHIA 6.115 (default parameters with
  PT(hard)gt3 GeV/c).
• Only charged particles with h lt 1 and PT gt 0.5
  GeV are included and the QCD Monte-Carlo
  predictions have been corrected for efficiency.

16
Transverse PTsum versus PT(chgjet1)
Isajet 7.32
Pythia 6.115
Herwig 5.9

• Plot shows the transverse ltPTsumgt versus
  PT(chgjet1) compared to the the QCD hard
  scattering predictions of HERWIG 5.9, ISAJET
  7.32, and PYTHIA 6.115 (default parameters with
  PT(hard)gt3 GeV/c).
• Only charged particles with h lt 1 and PT gt 0.5
  GeV are included and the QCD Monte-Carlo
  predictions have been corrected for efficiency.

17
ISAJET Transverse Nchg versus PT(chgjet1)
ISAJET
Initial-State Radiation
Beam-Beam Remnants
Outgoing Jets

• Plot shows the transverse ltNchggt vs
  PT(chgjet1) 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 three
  categories charged particles that arise from the
  break-up of the beam and target (beam-beam
  remnants), charged particles that arise from
  initial-state radiation, and charged particles
  that result from the outgoing jets plus
  final-state radiation.

18
ISAJET Transverse Nchg versus PT(chgjet1)
ISAJET
Outgoing Jets plus Initial Final-State Radiatio
n
Beam-Beam Remnants

• Plot shows the transverse ltNchggt vs
  PT(chgjet1) 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).

19
HERWIG Transverse Nchg versus PT(chgjet1)
HERWIG
Outgoing Jets plus Initial Final-State Radiatio
n
Beam-Beam Remnants

• Plot shows the transverse ltNchggt vs
  PT(chgjet1) compared to the QCD hard scattering
  predictions of HERWIG 5.9 (default parameters
  with PT(hard)gt3 GeV/c).
• 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).

20
PYTHIA Transverse Nchg versus PT(chgjet1)
PYTHIA
Outgoing Jets plus Initial Final-State Radiatio
n
Beam-Beam Remnants plus Multiple Parton
Interactions

• Plot shows the transverse ltNchggt vs
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA 6.115 (default parameters
  with PT(hard)gt3 GeV/c).
• The predictions of PYTHIA are divided into two
  categories charged particles that arise from the
  break-up of the beam and target (beam-beam
  remnants including multiple parton interactions)
  and charged particles that arise from the
  outgoing jet plus initial and final-state
  radiation (hard scattering component).

21
Hard Scattering Component Transverse Nchg vs
PT(chgjet1)
ISAJET
PYTHIA
HERWIG

• QCD hard scattering predictions of HERWIG 5.9,
  ISAJET 7.32, and PYTHIA 6.115.
• Plot shows the transverse ltNchggt vs
  PT(chgjet1) arising from the outgoing jets plus
  initial and finial-state radiation (hard
  scattering component).
• HERWIG and PYTHIA modify the leading-log picture
  to include color coherence effects which leads
  to angle ordering within the parton shower.
  Angle ordering produces less high PT radiation
  within a parton shower.

22
ISAJET TransversePT Distribution
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
3.7
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
2.0

• Data on the transverse ltNchggt versus PT(charged
  jet1) and the PT distribution of the
  transverse ltNchggt, dNchg/dPT, compared with the
  QCD Monte-Carlo predictions of ISAJET 7.32
  (default parameters with with PT(hard) gt 3
  GeV/c). The integral of dNchg/dPT is the
  transverse ltNchggt.

23
ISAJET TransversePT Distribution
exp(-2pT)

• Data on the PT distribution of the transverse
  ltNchggt, dNchg/dPT, compared with the QCD
  Monte-Carlo predictions of ISAJET 7.32 (default
  parameters with with PT(hard) gt 3 GeV/c). The
  dashed curve is the beam-beam remnant component
  and the solid curve is the total (beam-beam
  remnants plus hard component).

24
Tuned ISAJET TransversePT Distribution
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
3.2
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
2.1

• Data on the transverse ltNchggt versus PT(charged
  jet1) and the PT distribution of the
  transverse ltNchggt, dNchg/dPT, compared with the
  QCD Monte-Carlo predictions of Tuned ISAJET 7.32
  (PT(hard) gt 3 GeV/c, CUTJET 12 GeV, e-2PT
  beam-beam remnants). The integral of dNchg/dPT is
  the transverse ltNchggt.

Default 6 GeV
25
Tuned ISAJET TransversePT Distribution
exp(-2pT)

• Data on the PT distribution of the transverse
  ltNchggt, dNchg/dPT, compared with the QCD
  Monte-Carlo predictions of Tuned ISAJET 7.32
  (PT(hard) gt 3 GeV/c, CUTJET 12 GeV, e-2PT
  beam-beam remnants). The dashed curve is the
  beam-beam remnant component and the solid curve
  is the total (beam-beam remnants plus hard
  component).

26
Tuned ISAJET TransverseNchg and PTsum vs
PT(chgjet1)
Transverse ltNchggt
Transverse ltPTsumgt

• Data on the transverse Nchg and PTsum vs
  PT(chgjet1) compared with the QCD Monte-Carlo
  predictions of ISAJET 7.32 (with PT(hard) gt 3
  GeV/c).
• Tuned ISAJET has CUTJET 12 GeV (default 6
  GeV) and e-2PT beam-beam remnants (default is
  1/(PT2PT02)4).

27
HERWIG TransversePT Distribution
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
2.2
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
1.7

• Data on the transverse ltNchggt versus PT(charged
  jet1) and the PT distribution of the
  transverse ltNchggt, dNchg/dPT, compared with the
  QCD Monte-Carlo predictions of HERWIG 5.9
  (default parameters with with PT(hard) gt 3
  GeV/c). The integral of dNchg/dPT is the
  transverse ltNchggt.

28
HERWIG TransversePT Distribution
exp(-2pT)
same

• Data on the PT distribution of the transverse
  ltNchggt, dNchg/dPT, compared with the QCD
  Monte-Carlo predictions of HERWIG 5.9 (default
  parameters with with PT(hard) gt 3 GeV/c). The
  dashed curve is the beam-beam remnant component
  and the solid curve is the total (beam-beam
  remnants plus hard component).

29
HERWIG TransMAX/MIN vs PT(chgjet1)
TransMAX ltNchggt
TransMIN ltNchggt

• Data on the transMAX/MIN Nchg vs PT(chgjet1)
  compared with the QCD Monte-Carlo predictions of
  HERWIG 5.9 (default parameters with PT(hard) gt 3
  GeV/c).
• 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 jets plus initial and final-state
  radiation (hard scattering component).

30
HERWIG 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 5.9 (default parameters
  with PT(hard) gt 3 GeV/c).

31
PYTHIA TransversePT Distribution
Includes Multiple Parton Interactions
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
2.9
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
2.3

• Data on the transverse ltNchggt versus PT(charged
  jet1) and the PT distribution of the
  transverse ltNchggt, dNchg/dPT, compared with the
  QCD Monte-Carlo predictions of PYTHIA 6.115
  (default parameters with with PT(hard) gt 3
  GeV/c). The integral of dNchg/dPT is the
  transverse ltNchggt.

32
PYTHIA Multiple PartonInteractions
Pythia uses multiple parton interactions to
enhace the underlying event.
and new HERWIG!
Multiple parton interaction more likely in a hard
(central) collision!
Hard Core
33
PYTHIAMultiple Parton Interactions
PYTHIA default parameters
6.115
6.125
No multiple scattering

• Plot shows Transverse ltNchggt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 3 GeV.
• PYTHIA 6.115 GRV94L, MSTP(82)1,
  PTminPARP(81)1.4 GeV/c.
• PYTHIA 6.125 GRV94L, MSTP(82)1,
  PTminPARP(81)1.9 GeV/c.
• PYTHIA 6.115 GRV94L, MSTP(81)0, no multiple
  parton interactions.

Constant Probability Scattering
34
PYTHIAMultiple Parton Interactions
Note Multiple parton interactions depend
sensitively on the PDFs!

• Plot shows transverse ltNchggt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 0 GeV/c.
• PYTHIA 6.115 GRV94L, MSTP(82)1,
  PTminPARP(81)1.4 GeV/c.
• PYTHIA 6.115 CTEQ3L, MSTP(82)1, PTmin
  PARP(81)1.4 GeV/c.
• PYTHIA 6.115 CTEQ3L, MSTP(82)1, PTmin
  PARP(81)0.9 GeV/c.

Constant Probability Scattering
35
PYTHIAMultiple Parton Interactions
Note Multiple parton interactions depend
sensitively on the PDFs!

• Plot shows transverse ltNchggt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 0 GeV/c.
• PYTHIA 6.115 GRV94L, MSTP(82)3,
  PT0PARP(82)1.55 GeV/c.
• PYTHIA 6.115 CTEQ3L, MSTP(82)3,
  PT0PARP(82)1.55 GeV/c.
• PYTHIA 6.115 CTEQ3L, MSTP(82)3,
  PT0PARP(82)1.35 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)3,
  PT0PARP(82)1.8 GeV/c.

Varying Impact Parameter
36
PYTHIAMultiple Parton Interactions
Note Multiple parton interactions depend
sensitively on the PDFs!

• Plot shows transverse ltNchggt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 0 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)4,
  PT0PARP(82)1.55 GeV/c.
• PYTHIA 6.115 CTEQ3L, MSTP(82)4,
  PT0PARP(82)1.55 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)4,
  PT0PARP(82)2.4 GeV/c.

Varying Impact Parameter Hard Core
37
Tuned PYTHIA Transverse Nchg vs PT(chgjet1)
Describes correctly the rise from soft-collisions
to hard-collisions!

• Plot shows transverse ltNchggt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 0 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)3,
  PT0PARP(82)1.8 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)4,
  PT0PARP(82)2.4 GeV/c.

Varying Impact Parameter
38
Tuned PYTHIATransverse PTsum vs PT(chgjet1)
Describes correctly the rise from soft-collisions
to hard-collisions!

• Plot shows transverse ltPTsumgt versus
  PT(chgjet1) compared to the QCD hard scattering
  predictions of PYTHIA with PT(hard) gt 0 GeV.
• PYTHIA 6.115 CTEQ4L, MSTP(82)3,
  PT0PARP(82)1.8 GeV/c.
• PYTHIA 6.115 CTEQ4L, MSTP(82)4,
  PT0PARP(82)2.4 GeV/c.

Varying Impact Parameter
39
Tuned PYTHIATransverse PT Distribution
Includes Multiple Parton Interactions
PT(charged jet1) gt 30 GeV/c Transverse ltNchggt
2.7
PT(charged jet1) gt 5 GeV/c Transverse ltNchggt
2.3

• Data on the transverse ltNchggt versus PT(charged
  jet1) and the PT distribution of the
  transverse ltNchggt, dNchg/dPT, compared with the
  QCD Monte-Carlo predictions of PYTHIA 6.115 with
  PT(hard) gt 0 GeV/c, CTEQ4L, MSTP(82)4,
  PT0PARP(82)2.4 GeV/c. The integral of dNchg/dPT
  is the transverse ltNchggt.

40
Tuned PYTHIATransverse PT Distribution
Includes Multiple Parton Interactions

• Data on the PT distribution of the transverse
  ltNchggt, dNchg/dPT, compared with the QCD
  Monte-Carlo predictions of PYTHIA 6.115 with
  PT(hard) gt 0, CTEQ4L, MSTP(82)4,
  PT0PARP(82)2.4 GeV/c. The dashed curve is the
  beam-beam remnant component and the solid curve
  is the total (beam-beam remnants plus hard
  component).

41
Tuned PYTHIA 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 PYTHIA 6.115 with PT(hard) gt 0,
  CTEQ4L, MSTP(82)4, PT0PARP(82)2.4 GeV/c.

42
Tuned PYTHIA TransSUM/DIF vs PT(chgjet1)
ltNchggt
ltPTsumgt

• Plots shows data on the transSUM/DIF ltNchggt and
  transSUM/DIF 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 PYTHIA 6.115 with PT(hard) gt 0,
  CTEQ4L, MSTP(82)4, PT0PARP(82)2.4 GeV/c.

43
Tuned PYTHIA TransMAX/MIN vs PT(chgjet1)
TransMAX ltNchggt
TransMIN ltNchggt
Includes Multiple Parton Interactions

• Data on the transMAX/MIN Nchg vs PT(chgjet1)
  comared with the QCD Monte-Carlo predictions of
  PYTHIA 6.115 with PT(hard) gt 0, CTEQ4L,
  MSTP(82)4, PT0PARP(82)2.4 GeV/c.
• The predictions of PYTHIA 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 jets plus initial and final-state
  radiation (hard scattering component).

44
Tuned PYTHIA TransSUM/DIF vs PT(chgjet1)
TransSUM ltNchggt
TransDIF ltNchggt
Includes Multiple Parton Interactions

• Data on the transSUM/DIF Nchg vs PT(chgjet1)
  comared with the QCD Monte-Carlo predictions of
  PYTHIA 6.115 with PT(hard) gt 0, CTEQ4L,
  MSTP(82)4, PT0PARP(82)2.4 GeV/c.
• The predictions of PYTHIA 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).

45
The Underlying EventSummary Conclusions
The Underlying Event

• Combining the two CDF analyses gives a
  quantitative study of the underlying event from
  very soft collisions to very hard collisions.
• ISAJET (with independent fragmentation) produces
  too many (soft) particles in the underlying event
  with the wrong dependence on PT(jet1). HERWIG
  and PYTHIA modify the leading-log picture to
  include color coherence effects which leads to
  angle ordering within the parton shower and do
  a better job describing the underlying event.
• Both ISAJET and HERWIG have the too steep of a PT
  dependence of the beam-beam remnant component of
  the underlying event and hence do not have enough
  beam-beam remnants with PT gt 0.5 GeV/c.
• PYTHIA (with multiple parton interactions) does
  the best job in describing the underlying event.
• Perhaps the multiple parton interaction approach
  is correct or maybe we simply need to improve the
  way the Monte-Carlo models handle the beam-beam
  remnants (or both!).

46
Multiple Parton InteractionsSummary
Conclusions
Multiple Parton Interactions
Proton
AntiProton
Hard Core
Hard Core

• The increased activity in the underlying event in
  a hard scattering over a soft collision cannot be
  explained by initial-state radiation.
• Multiple parton interactions gives a natural way
  of explaining the increased activity in the
  underlying event in a hard scattering. A hard
  scattering is more likely to occur when the hard
  cores overlap and this is also when the
  probability of a multiple parton interaction is
  greatest. For a soft grazing collision the
  probability of a multiple parton interaction is
  small.
• PYTHIA (with varying impact parameter) describes
  the underlying event data fairly well. However,
  there are problems in fitting min-bias events
  with this approach.
• Multiple parton interactions are very sensitive
  to the parton structure functions. You must
  first decide on a particular PDF and then tune
  the multiple parton interactions to fit the data.

Slow!