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The Underlying Event in Hard Scattering Processes

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Les Houches 2001. Rick Field - Florida/CDF. Page 1. The Underlying Event in ... Les Houches 2001. Rick Field - Florida/CDF. Page 11 'Max/Min Transverse' Nchg ... – PowerPoint PPT presentation

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Title: 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
Multiple Parton Interactions
  • 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
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.

6
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.

7
Shape of an AverageEvent with PT(chgjet1) 20
GeV/c
Includes Jet1
Underlying event plateau
Remember h lt 1 PT gt 0.5 GeV
Shape in Nchg
8
Height of the UnderlyingEvent Plateau
Implies 1.093(2.4)/2 3.9 charged particles per
unit h with PT gt 0.5 GeV/c.
Hard Soft
Implies 2.33.9 9 charged particles per unit
h with PT gt 0 GeV/c which is a factor of 2
larger than soft collisions.
4 per unit h
9
Transverse PT Distribution
  • Plot shows the PT distribution of the
    Transverse ltNchggt, dNchg/dPT. The integral of
    dNchg/dPT is the Transverse ltNchggt.
  • The triangle and circle (square) 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
TransSUM/DIF Nchg versus PT(chgjet1)
Area DhDf 2x60o 2p/3
TransSUM
TransDIF
  • Define TransMAX and TransMIN to be the
    maximum and minimum of the region 60oltDflt120o
    (60olt-Dflt120o) on an event by event basis. The
    plot shows the average sum and difference of the
    TransMAX Nchg and the TransMIN Nchg versus
    PT(charged jet1). The overall transverse
    region is the sum of TransMAX and TransMIN.
  • The solid (open) points are the Min-Bias (JET20)
    data. The errors on the (uncorrected) data
    include both statistical and correlated
    systematic uncertainties.

13
Max/Min Transverse PTsum 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 PTsum and TransMIN PTsum
    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.

14
TransSUM/DIF PTsum versus PT(chgjet1)
Area DhDf 2x60o 2p/3
TransSUM
TransDIF
  • Define TransMAX and TransMIN to be the
    maximum and minimum of the region 60oltDflt120o
    (60olt-Dflt120o) on an event by event basis. The
    plot shows the average sum and difference of the
    TransMAX PTsum and the TransMIN PTsum versus
    PT(charged jet1). The overall transverse
    region is the sum of TransMAX and TransMIN.
  • The solid (open) points are the Min-Bias (JET20)
    data. The errors on the (uncorrected) data
    include both statistical and correlated
    systematic uncertainties.

15
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

16
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..

17
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!
18
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.

19
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.

20
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.

21
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).

22
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).

23
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).

24
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.

25
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.

26
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).

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
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.

30
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
31
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
32
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
33
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
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 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
35
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
36
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
37
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.

38
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).

39
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.

40
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.

41
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).

42
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).

43
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!).

44
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.

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