qcd monte-carlo generators in run 2 at cdf

CDF Top Mass Workshop June 25, 2003

Rick Field

QCD Monte-Carlo GeneratorsQCD Monte-Carlo Generators

in Run 2 at CDFin Run 2 at CDF

Review what we learned in Run 1 about “min-bias”, the “underlying event”, and “initial-state radiation”.

Outline of Talk

Compare the Run 1 analysis which used the leading “charged particle jet” to define the “underlying event” with Run 2 data.

Study the properties of “charged particle jets” and “calorimeter jets” in Run 2.

Proton AntiProton

“Hard” Scattering

PT(hard)

Outgoing Parton

Outgoing Parton

Underlying Event Underlying Event

Initial-State Radiation

Final-State Radiation

Study the “underlying event” in Run 2 as defined by the leading “calorimeter jet” and compare with the “charged particle jet” analysis.

Charged Particle Jet

Calorimeter Jet Compare with PYTHIA

Tune A

JetClu R = 0.7

AlsoI have included some

thoughts from Michelangeloat the end of my talk.


Rick Field

The “Underlying Event”The “Underlying Event”in Hard Scattering Processesin Hard Scattering Processes

What happens when a high energy proton and an antiproton collide? Proton AntiProton

“Soft” Collision (no hard scattering)

Proton AntiProton


PT(hard)

Outgoing Parton

Outgoing Parton




Proton AntiProton 2 TeV Most of the time the proton and

antiproton ooze through each other and fall apart (i.e. no hard scattering). The outgoing particles continue in roughly the same direction as initial proton and antiproton. A “Min-Bias” collision.

Occasionally there will be a “hard” parton-parton collision resulting in large transverse momentum outgoing partons. Also a “Min-Bias” collision.

Proton AntiProton

“Underlying Event”

Beam-Beam Remnants Beam-Beam Remnants


The “underlying event” is everything except the two outgoing hard scattered “jets”. It is an unavoidable background to many collider observables.

Arethesethe

same?

“Min-Bias”

No!

“underlying event” has initial-state radiation!


Rick Field

Beam-Beam RemnantsBeam-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”).

Proton AntiProton

“Hard” Collision

initial-state radiation

final-state radiation outgoing parton

outgoing parton

Clearly? the “underlying event” in a hard scattering process should not look like a “Min-Bias” event because of the “hard” component (i.e. initial & final-state radiation).

+

“Soft?” Component “Hard” Component


final-state radiation outgoing jet

Beam-Beam Remnants

“Soft?” Component

Beam-Beam Remnants

Hadron Hadron

“Min-Bias” Collision

However, perhaps “Min-Bias” collisions are a good model for the “beam-beam remnant” component of the “underlying event”.

Are these the same?

The “beam-beam remnant” component is, however, color connected to the “hard” component so this comparison is (at best) an approximation.

color string

color string

Maybe not all “soft”!


Rick Field

MPI: Multiple PartonMPI: Multiple PartonInteractionsInteractions

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”.

Proton AntiProton

Multiple Parton Interaction



outgoing parton

color string

color string

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).

+

“Semi-Hard” MPI “Hard” Component


final-state radiation outgoing jet Beam-Beam Remnants

or

“Soft” Component

Proton AntiProton

“Hard” Collision



outgoing parton


Rick Field

CDF Run 1 “Min-Bias” DataCDF Run 1 “Min-Bias” DataCharged Particle DensityCharged Particle Density

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 in “Min-Bias” collisions at 1.8 TeV (|| < 1, all PT).

Charged Particle Pseudo-Rapidity Distribution: dN/d

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7

-4 -3 -2 -1 0 1 2 3 4

Pseudo-Rapidity

dN

/d

CDF Min-Bias 1.8 TeV

CDF Min-Bias 630 GeV all PT

CDF Published

<dNchg/d> = 4.2

Charged Particle Density: dN/dd

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1.0

-4 -3 -2 -1 0 1 2 3 4

Pseudo-Rapidity

dN

/d d

CDF Min-Bias 630 GeV

CDF Min-Bias 1.8 TeV all PT

CDF Published

<dNchg/dd> = 0.67

Convert to charged particle density, dNchg/dd by dividing by 2. There are about 0.67 charged particles per unit - in “Min-Bias” collisions at 1.8 TeV (|| < 1, all PT).

= 1

= 1

x = 1

0.67 There are about 0.25 charged particles per unit - in “Min-Bias” collisions at 1.8 TeV (|| < 1, PT > 0.5 GeV/c).

0.25


Rick Field

CDF Run 1 “Min-Bias” DataCDF Run 1 “Min-Bias” DataPPTT Dependence Dependence

Charged Particle Density

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||<1CDF Preliminary

CDF Min-Bias Data at 1.8 TeV

HW "Soft" Min-Biasat 630 GeV, 1.8 TeV, and 14 TeV


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Pseudo-Rapidity

dN

/d d

630 GeV1.8 TeV

Herwig "Soft" Min-Bias 14 TeV

all PT

Shows the PT dependence of the charged particle density, dNchg/dddPT, for “Min-Bias” collisions at 1.8 TeV collisions compared with HERWIG “Soft” Min-Bias.

HERWIG “Soft” Min-Bias does not describe the “Min-Bias” data! The “Min-Bias” data contains a lot of “hard” parton-parton collisions which results in many more particles at large PT than are produces by any “soft” model.

Shows the energy dependence of the charged particle density, dNchg/dd for “Min-Bias” collisions compared with HERWIG “Soft” Min-Bias.

Lots of “hard” scattering in “Min-Bias”!


Rick Field

Min-Bias: CombiningMin-Bias: Combining“Hard” and “Soft” Collisions“Hard” and “Soft” Collisions


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CDF Min-Bias Data Herwig Jet3 Herwig Min-Bias

1.8 TeV all PTHW "Soft" Min-Bias

HW PT(hard) > 3 GeV/c


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Herwig Jet3Herwig Min-BiasCDF Min-Bias Data

1.8 TeV ||<1

CDF Preliminary

HW PT(hard) > 3 GeV/c

HW "Soft" Min-Bias

HERWIG “hard” QCD with PT(hard) > 3 GeV/c describes well the high PT tail but produces too many charged particles overall. Not all of the “Min-Bias” collisions have a hard scattering with PT(hard) > 3 GeV/c!

One cannot run the HERWIG “hard” QCD Monte-Carlo with PT(hard) < 3 GeV/c because the perturbative 2-to-2 cross-sections diverge like 1/PT(hard)4?

HERWIG “soft” Min-Bias does not fit the “Min-Bias” data!

No easy way to“mix” HERWIG “hard” with HERWIG “soft”.

Hard-Scattering Cross-Section

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rns

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PYTHIA

HERWIG

CTEQ5L1.8 TeV

HC

HERWIG diverges!

PYTHIA cuts off the divergence.

Can run PT(hard)>0!


Rick Field

PYTHIA Min-BiasPYTHIA Min-Bias“Soft” + ”Hard”“Soft” + ”Hard”


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Pseudo-Rapidity

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Pythia 6.206 Set A

CDF Min-Bias 1.8 TeV 1.8 TeV all PT

CDF Published

PYTHIA regulates the perturbative 2-to-2 parton-parton cross sections with cut-off parameters which allows one to run with PT(hard) > 0. One can simulate both “hard” and “soft” collisions in one program.

The relative amount of “hard” versus “soft” depends on the cut-off and can be tuned.


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Pythia 6.206 Set A

CDF Min-Bias Data

CDF Preliminary

1.8 TeV ||<1

PT(hard) > 0 GeV/c

Tuned to fit the “underlying event”!

12% of “Min-Bias” events have PT(hard) > 5 GeV/c!

1% of “Min-Bias” events have PT(hard) > 10 GeV/c!

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) > 5 GeV/c (1% with PT(hard) > 10 GeV/c)!

Lots of “hard” scattering in “Min-Bias”!

PYTHIA Tune ACDF Run 2 Default


Rick Field

““Underlying Event”Underlying Event”as defined by “Charged particle Jets”as defined by “Charged particle Jets”

Charged Jet #1Direction

“Transverse” “Transverse”

“Toward”

“Away”

“Toward-Side” Jet

“Away-Side” Jet

Look at charged particle correlations in the azimuthal angle relative to the leading charged particle jet.

Define || < 60o as “Toward”, 60o < || < 120o as “Transverse”, and || > 120o as “Away”. All three regions have the same size in - space, x = 2x120o = 4/3.


“Toward”


“Away”

Charged Particle Correlations PT > 0.5 GeV/c || < 1

Toward-side “jet”(always)

Away-side “jet”(sometimes)

Perpendicular to the plane of the 2-to-2 hard scattering

“Transverse” region is very sensitive to the “underlying event”!

-1 +1

2

0

Leading ChgJet

Toward Region

Transverse Region

Transverse Region

Away Region

Away Region

Look at the charged particle density in the “transverse” region!


Rick Field

Run 1 Charged Particle DensityRun 1 Charged Particle Density

“Transverse” P“Transverse” PTT Distribution Distribution

Compares the average “transverse” charge particle density with the average “Min-Bias” charge particle density (||<1, PT>0.5 GeV). Shows how the “transverse” charge particle density and the Min-Bias charge particle density is distributed in PT.

CDF Run 1 Min-Bias data<dNchg/dd> = 0.25

PT(charged jet#1) > 30 GeV/c“Transverse” <dNchg/dd> = 0.56

Factor of 2!

“Min-Bias”

"Transverse" Charged Particle Density: dN/dd

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PT(charged jet#1) (GeV/c)

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CDF Min-Bias

CDF JET20CDF Run 1data uncorrected

1.8 TeV ||<1.0 PT>0.5 GeV/c

Charged Particle Jet #1 Direction

“Toward”


“Away”


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CDF Run 1data uncorrected

1.8 TeV ||<1 PT>0.5 GeV/c

Min-Bias

"Transverse"PT(chgjet#1) > 5 GeV/c



Rick Field

ISAJET 7.32ISAJET 7.32“Transverse” Density“Transverse” Density

Compares the average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) and the PT distribution of the “transverse” density, dNchg/dddPT with the QCD hard scattering predictions of ISAJET 7.32 (default parameters with PT(hard)>3 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 initial-state radiation, and charged particles that arise from the outgoing jets plus final-state radiation..

Beam-BeamRemnants

ISAJETCharged Jet #1Direction

“Toward”


“Away”

Initial-StateRadiation


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CDF Run 1 Datadata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

Isajet

IS Radiation

"Remnants"

Jets + FS

ISAJET uses a naïve leading-log parton shower-model which does

not agree with the data!


Rick Field

ISAJET 7.32ISAJET 7.32“Transverse” Density“Transverse” Density

Plot shows average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) compared to the QCD hard scattering predictions of ISAJET 7.32 (default parameters with PT(hard)>3 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).

Beam-BeamRemnants

ISAJETCharged Jet #1Direction

“Toward”


“Away”

“Hard”Component


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CDF Run 1Datadata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

Isajet

"Remnants"

"Hard"

ISAJET uses a naïve leading-log parton shower-model which does

not agree with the data!


Rick Field

HERWIG 6.4HERWIG 6.4“Transverse” Density“Transverse” Density

Plot shows average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) compared to the QCD hard scattering predictions of HERWIG 5.9 (default parameters with PT(hard)>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 jet plus initial and final-state radiation (hard scattering component).

Beam-BeamRemnants

HERWIG


“Toward”


“Away”


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y CDF Run 1Datadata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

Herwig 6.4 CTEQ5LPT(hard) > 3 GeV/c

Total "Hard"

"Remnants"

“Hard”Component


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CDF Run 1Datadata uncorrectedtheory corrected

1.8 TeV ||<1.0 PT>0.5 GeV

Isajet

"Remnants"

"Hard"

HERWIG uses a modified leading-log parton shower-model which

does agrees better with the data!


Rick Field

HERWIG 6.4HERWIG 6.4“Transverse” P“Transverse” PTT Distribution Distribution

"Transverse" Charged Particle Density

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CDF Datadata uncorrectedtheory corrected

1.8 TeV ||<1 PT>0.5 GeV/c

PT(chgjet#1) > 5 GeV/c


Herwig 6.4 CTEQ5L

Herwig PT(chgjet#1) > 5 GeV/c<dNchg/dd> = 0.40

Herwig PT(chgjet#1) > 30 GeV/c“Transverse” <dNchg/dd> = 0.51


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1.8 TeV ||<1.0 PT>0.5 GeV

Herwig 6.4 CTEQ5LPT(hard) > 3 GeV/c

Total "Hard"

"Remnants"

Compares the average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) and the PT distribution of the “transverse” density, dNchg/dddPT with the QCD hard scattering predictions of HERWIG 6.4 (default parameters with PT(hard)>3 GeV/c. Shows how the “transverse” charge particle density is distributed in PT.

HERWIG has the too steep of a PT dependence of the “beam-beam remnant”

component of the “underlying event”! Charged Jet #1Direction

“Toward”


“Away”


Rick Field

PYTHIA: Multiple PartonPYTHIA: Multiple PartonInteraction ParametersInteraction Parameters

Pythia uses multiple partoninteractions to enhancethe underlying event.

Parameter Value

Description

MSTP(81) 0 Multiple-Parton Scattering off

1 Multiple-Parton Scattering on

MSTP(82) 1 Multiple interactions assuming the same probability, with an abrupt cut-off PTmin=PARP(81)

3 Multiple interactions assuming a varying impact parameter and a hadronic matter overlap consistent with a single Gaussian matter distribution, with a smooth turn-off PT0=PARP(82)

4 Multiple interactions assuming a varying impact parameter and a hadronic matter overlap consistent with a double Gaussian matter distribution (governed by PARP(83) and PARP(84)), with a smooth turn-off PT0=PARP(82)

Hard Core

Multiple parton interaction more likely in a hard

(central) collision!

and now HERWIG

!

Jimmy: MPIJ. M. Butterworth

J. R. ForshawM. H. Seymour

Proton AntiProton

Multiple Parton Interactions

PT(hard)

Outgoing Parton

Outgoing Parton

Underlying EventUnderlying Event

Same parameter that cuts-off the hard 2-to-2 parton cross sections!


Rick Field

Tuning PYTHIA:Tuning PYTHIA:Multiple Parton Interaction ParametersMultiple 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(90) 0.16 Determines the energy dependence of the cut-off

PT0 as follows PT0(Ecm) = PT0(Ecm/E0) with = 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


Color String

Color String


Color String

Hard-Scattering Cut-Off PT0

1

2

3

4

5

100 1,000 10,000 100,000

CM Energy W (GeV)

PT

0

(Ge

V/c

)

PYTHIA 6.206

= 0.16 (default)

= 0.25 (Set A))

Take E0 = 1.8 TeV

Reference pointat 1.8 TeV

Determine by comparingwith 630 GeV data!

Affects the amount ofinitial-state radiation!


Rick Field


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PT(charged jet#1) (GeV/c)"T

ran

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se"

Ch

arg

ed D

ensi

ty

CTEQ3L CTEQ4L CTEQ5L CDF Min-Bias CDF JET20

1.8 TeV ||<1.0 PT>0.5 GeV

Pythia 6.206 (default)MSTP(82)=1

PARP(81) = 1.9 GeV/c


Default parameters give very poor description of the “underlying event”!

Note ChangePARP(67) = 4.0 (< 6.138)PARP(67) = 1.0 (> 6.138)

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

PYTHIA 6.206 DefaultsPYTHIA 6.206 Defaults

Plot shows the “Transverse” charged particle density versus PT(chgjet#1) compared to the QCD hard scattering predictions of PYTHIA 6.206 (PT(hard) > 0) using the default parameters for multiple parton interactions and CTEQ3L, CTEQ4L, and CTEQ5L.

PYTHIA default parameters

MPI constantprobabilityscattering


Rick Field

Old PYTHIA default(more initial-state radiation)

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

Tuned PYTHIA 6.206Tuned PYTHIA 6.206

Plot shows the “Transverse” charged particle density versus PT(chgjet#1) 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)).


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CDF Preliminarydata uncorrectedtheory corrected

CTEQ5L

PYTHIA 6.206 (Set A)PARP(67)=4

PYTHIA 6.206 (Set B)PARP(67)=1

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

PYTHIA 6.206 CTEQ5L

New PYTHIA default(less initial-state radiation)

New PYTHIA default(less initial-state radiation)

Double Gaussian

Old PYTHIA default(more initial-state radiation)

Tune A CDFRun 2 Default!


Rick Field

Azimuthal CorrelationsAzimuthal Correlations

Predictions of PYTHIA 6.206 (CTEQ5L) with PARP(67)=1 (new default) and PARP(67)=4 (old default) for the azimuthal angle, , between a b-quark with PT1 > 15 GeV/c, |y1| < 1 and bbar-quark with PT2 > 10 GeV/c, |y2|<1 in proton-antiproton collisions at 1.8 TeV. The curves correspond to d/d (b/o) for flavor creation, flavor excitation, shower/fragmentation, and the resulting total.

b-quark Correlations: Azimuthal Distribution

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(degrees)

d /

d

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/deg

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PY62 (67=1) Total Flavor Creation Flavor Excitation Shower/Fragmentation

1.8 TeVPT1 > 15 GeV/cPT2 > 10 GeV/c|y1| < 1 |y2| < 1

PYTHIA 6.206 CTEQ5L PARP(67)=1

"Away""Toward"

b-quark direction

“Toward”

“Away”

bbar-quark


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PY62 (67=4) Total Flavor Creation Flavor Excitation Shower/Fragmentation


PYTHIA 6.206 CTEQ5L PARP(67)=4

"Away""Toward"

PYTHIA Tune B(less initial-state radiation)

PYTHIA Tune A(more initial-state radiation)


Rick Field

Azimuthal CorrelationsAzimuthal Correlations

Predictions of HERWIG 6.4 (CTEQ5L) for the azimuthal angle, , between a b-quark with PT1 > 15 GeV/c, |y1| < 1 and bbar-quark with PT2 > 10 GeV/c, |y2|<1 in proton-antiproton collisions at 1.8 TeV. The curves correspond to d/d (b/o) for flavor creation, flavor excitation, shower/fragmentation, and the resulting total.


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g)

HW64 Total Flavor Creation Flavor Excitation Shower/Fragmentation


HERWIG 6.4 CTEQ5L

"Away""Toward"

b-quark direction

“Toward”

“Away”

bbar-quark


0.000001

0.000010

0.000100

0.001000

0.010000

0 30 60 90 120 150 180

(degrees)

d /

d

(b

/deg

)


"Flavor Creation" CTEQ5L

"Away""Toward"

HERWIG 6.4

PYTHIA 6.206PARP(67)=1

PYTHIA 6.206PARP(67)=4

“Flavor Creation”

PYTHIA Tune B(less initial-state radiation)

PYTHIA Tune A(more initial-state radiation)


Rick Field Page 22

Tuned PYTHIA 6.206Tuned PYTHIA 6.206“Transverse” P“Transverse” PTT Distribution Distribution


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1.8 TeV ||<1.0 PT>0.5 GeV


CTEQ5L

PYTHIA 6.206 (Set A)PARP(67)=4

PYTHIA 6.206 (Set B)PARP(67)=1

PARP(67)=4.0 (old default) is favored over PARP(67)=1.0 (new default)!

PT(charged jet#1) > 30 GeV/c

Can we distinguish between PARP(67)=1 and PARP(67)=4?

No way! Right!

"Transverse" Charged Particle Density

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 2 4 6 8 10 12 14


har

ged

Den

sity

dN

/d d

dP

T (

1/G

eV/c

)


1.8 TeV ||<1 PT>0.5 GeV/c



PYTHIA 6.206 Set APARP(67)=4

PYTHIA 6.206 Set BPARP(67)=1

Compares the average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) and the PT distribution of the “transverse” density, dNchg/dddPT with the QCD Monte-Carlo predictions of two tuned versions of PYTHIA 6.206 (PT(hard) > 0, CTEQ5L, Set B (PARP(67)=1) and Set A (PARP(67)=4)).


Rick Field Page 24


1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 2 4 6 8 10 12 14


har

ged

Den

sity

dN

/d d

dP

T (

1/G

eV/c

)

CDF Run 1data uncorrectedtheory corrected

1.8 TeV ||<1 PT>0.5 GeV/c

CDF Min-Bias



PYTHIA 6.206 Set A

CTEQ5L


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1.8 TeV ||<1.0 PT>0.5 GeV/c

CDF Run 1data uncorrectedtheory corrected

PYTHIA 6.206 Set A

Tuned PYTHIA 6.206Tuned PYTHIA 6.206Run 1 Tune ARun 1 Tune A

Compares the average “transverse” charge particle density (||<1, PT>0.5 GeV) versus PT(charged jet#1) and the PT distribution of the “transverse” and “Min-Bias” densities with the QCD Monte-Carlo predictions of a tuned version of PYTHIA 6.206 (PT(hard) > 0, CTEQ5L, Set A).

Set A Min-Bias<dNchg/dd> = 0.24


0.0

0.2

0.4

0.6

0.8

1.0

-4 -3 -2 -1 0 1 2 3 4

Pseudo-Rapidity

dN

/d d

Pythia 6.206 Set A

CDF Min-Bias 1.8 TeV 1.8 TeV all PT

CDF Published

Describes “Min-Bias” collisions! Describes the “underlying event”!

“Min-Bias”

Set A PT(charged jet#1) > 30 GeV/c“Transverse” <dNchg/dd> = 0.60

Describes the rise from “Min-Bias” to “underlying event”!


Rick Field Page 27


“Toward”


“Away”


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ran

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Ch

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CDF Run 1 Min-Bias

CDF Run 1 JET20CDF Run 1 Data

data uncorrected

1.8 TeV ||<1.0 PT>0.5 GeV


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CDF Run 1 Min-Bias

CDF Run 1 JET20

||<1.0 PT>0.5 GeV

CDF Preliminarydata uncorrected

“ “Transverse” Transverse” Charged Particle DensityCharged Particle Density

Shows the data on the average “transverse” charge particle density (||<1, PT>0.5 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.


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CDF Run 2

“Transverse” region as defined by the leading “charged particle jet”


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ensi

ty CDF Run 1 Published

CDF Run 2 Preliminary

||<1.0 PT>0.5 GeV/c


Excellent agreement between Run 1 and 2!


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CDF Run 1 Published


PYTHIA Tune A

||<1.0 PT>0.5 GeV/c


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).


Rick Field Page 28

“ “Transverse” Transverse” Charged PTsum DensityCharged PTsum Density

Shows the data on the average “transverse” charged PTsum density (||<1, PT>0.5 GeV) as a function of the transverse momentum of the leading charged particle jet from Run 1.

"Transverse" Charged PTsum Density: dPTsum/dd

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0 5 10 15 20 25 30 35 40 45 50


ran

sver

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PT

sum

Den

sity

(G

eV) CDF JET20

CDF Min-BiasCDF Run 1 Data

data uncorrected

1.8 TeV ||<1.0 PT>0.5 GeV



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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


ran

sver

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PT

sum

Den

sity

(G

eV)

CDF JET20

CDF Min-Bias


||<1.0 PT>0.5 GeV


“Toward”


“Away”


0.00

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1.00

1.25

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


ran

sver

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PT

sum

Den

sity

(G

eV)

CDF Run 2

“Transverse” region as defined by the leading “charged particle jet”


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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


ran

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PT

sum

Den

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(G

eV/c

)

CDF Run 1 Published



||<1.0 PT>0.5 GeV/c



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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


ran

sver

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PT

sum

Den

sity

(G

eV/c

)

CDF Run 1 Published


PYTHIA Tune A


||<1.0 PT>0.5 GeV/c

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

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


Rick Field Page 30

JetClu Jet #1 Direction


“Toward”

“Away”

“Toward-Side” Jet

“Away-Side” Jet

““Underlying Event”Underlying Event”as defined by “Calorimeter Jets”as defined by “Calorimeter Jets”

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

Define || < 60o as “Toward”, 60o < || < 120o as “Transverse”, and || > 120o as “Away”. All three regions have the same size in - space, x = 2x120o = 4/3.

Charged Particle Correlations PT > 0.5 GeV/c || < 1

Away-side “jet”(sometimes)

Perpendicular to the plane of the 2-to-2 hard scattering

“Transverse” region is very sensitive to the “underlying event”!


“Toward”


“Away”

-1 +1

2

0

Leading Jet

Toward Region

Transverse Region

Transverse Region

Away Region

Away Region

Look at the charged particle density in the “transverse” region!


Rick Field Page 31

““Transverse” Transverse” Charged Particle DensityCharged Particle Density

Shows the data on the average “transverse” charge particle density (||<1, PT>0.5 GeV) as a function of the transverse energy of the leading JetClu jet (R = 0.7, |(jet)| < 2) from Run 2.


“Toward”


“Away”

Compares the “transverse” region of the leading “charged particle jet”, chgjet#1, with the “transverse” region of the leading “calorimeter jet” (JetClu R = 0.7), jet#1.

JetClu Jet #1 or ChgJet#1

Direction

“Toward”


“Away”

, compared with PYTHIA Tune A after CDFSIM.


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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

"Tra

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erse

" C

har

ged

Den

sity


Charged Particles (||<1.0, PT>0.5 GeV/c)

JetClu (R = 0.7, |(jet#1)| < 2)

“Transverse” region as defined by the leading

“calorimeter jet” "Transverse" Charged Particle Density: dN/dd

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1.00

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

"Tra

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Den

sity

PYTHIA Tune A

CDF Run 2 PreliminaryCDF Preliminary

data uncorrectedtheory corrected


JetClu (R = 0.7, |(jet#1)| < 2)


0.00

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0.75

1.00

0 25 50 75 100 125 150 175 200 225 250

PT(chgjet#1) or ET(jet#1) (GeV)

"Tra

nsv

erse

" C

har

ged

Den

sity CDF Preliminary

data uncorrected


ChgJet#1 R = 0.7

JetClu Jet#1 (R = 0.7,|(jet)|<2)


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0 25 50 75 100 125 150 175 200 225 250


"Tra

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Den

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ChgJet#1 R = 0.7

JetClu Jet#1 (R = 0.7, |(jet)|<2)

PYTHIA Tune A 1.96 TeV


Rick Field Page 32

““Transverse” Transverse” Charged PTsum DensityCharged PTsum Density

Shows the data on the average “transverse” charged PTsum density (||<1, PT>0.5 GeV) as a function of the transverse energy of the leading JetClu jet (R = 0.7, |(jet)| < 2) from Run 2.


“Toward”


“Away”

Compares the “transverse” region of the leading “charged particle jet”, chgjet#1, with the “transverse” region of the leading “calorimeter jet” (JetClu R = 0.7), jet#1.

JetClu Jet #1 or ChgJet#1

Direction

“Toward”


“Away”



0.0

0.5

1.0

1.5

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

"Tra

nsv

erse

" P

Tsu

m D

ensi

ty (

GeV

/c)



JetClu (R = 0.7, |(jet#1)| < 2)


“calorimeter jet” "Transverse" Charged PTsum Density: dPTsum/dd

0.0

0.5

1.0

1.5

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

"Tra

nsv

erse

" P

Tsu

m D

ensi

ty (

GeV

/c)

PYTHIA Tune A




JetClu (R = 0.7, |(jet#1)| < 2)


0.0

0.5

1.0

1.5

0 25 50 75 100 125 150 175 200 225 250


"Tra

nsv

erse

" P

Tsu

m D

ensi

ty (

GeV

/c)


Charged Particles (||<1.0, PT>0.5 GeV/c) ChgJet#1 R = 0.7



0.0

0.5

1.0

1.5

0 25 50 75 100 125 150 175 200 225 250


"Tra

nsv

erse

" P

Tsu

m D

ensi

ty (

GeV

/c)


Charged Particles (||<1.0, PT>0.5 GeV/c) ChgJet#1 R = 0.7




Rick Field Page 33

““Transverse” Transverse” Charged Particle DensityCharged Particle Density

Shows the data on the average “transverse” charge particle density (||<1, PT>0.5 GeV) as a function of the transverse energy of the leading JetClu jet (R = 0.7, |(jet)| < 2) from Run 2.


“Toward”


“Away”

Shows the generated prediction of PYTHIA Tune A before CDFSIM.



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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)"T

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JetClu (R = 0.7, |(jet#1)| < 2)


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ET(jet#1) (GeV)"T

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PYTHIA Tune A

CDF Run 2 PreliminaryCDF Preliminary

data uncorrectedtheory corrected


JetClu (R = 0.7, |(jet#1)| < 2)


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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)"T

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PY Tune A Generated

PY Tune A Corrected




JetClu (R = 0.7, |(jet#1)| < 2)


“calorimeter jet”

CDFSIM/Generated: "Transverse" dN/dd

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

CD

FS

IM/G

ener

ated

JetClu (R = 0.7, |(jet#1)| < 2)



Shows the ratio CDFSIM/Generated for PYTHIA Tune A.

Small correction (about 10%) independent of ET(jet#1)!


Rick Field Page 34

The Leading The Leading “Charged Particle” Jet“Charged Particle” Jet

Shows the data on the average number of charged particles within the leading “charged particle jet” (||<1, PT>0.5 GeV, R = 0.7) as a function of the transverse momentum of the leading “charged particle jet” from Run 1.

Nchg(chgjet#1) versus PT(chgjet#1)

0

2

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6

8

10

12

0 5 10 15 20 25 30 35 40 45 50


Ave

rave

Nch

g

CDF Run 1 Min-Bias

CDF Run 1 JET20

CDF Run 1 Datadata uncorrected

1.8 TeV ||<1.0 PT>0.5 GeV R = 0.7


0

2

4

6

8

10

12

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


Ave

rave

Nch

g

CDF Run 1 Min-Bias

CDF Run 1 JET20


||<1.0 PT>0.5 GeV R = 0.7



0

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12

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


Ave

rave

Nch

g

CDF Run 2


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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


Ave

rave

Nch

g

CDF Run 1 Min-Bias

CDF Run 1 JET20

CDF Run 2


||<1.0 PT>0.5 GeV/c R = 0.7


0

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0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150


Ave

rave

Nch

g

CDF Run 1 Min-Bias

CDF Run 1 JET20

CDF Run 2

PY Tune A

||<1.0 PT>0.5 GeV/c R = 0.7



PYTHIA produces too many charged particles in the leading

“charged particle jet”!


Rick Field Page 35

The Leading The Leading “Calorimeter” Jet“Calorimeter” Jet

Shows the Run 2 data on the average number of charged particles (||<1, PT>0.5 GeV, R = 0.7) within the leading “calorimeter jet” (JetClu R = 0.7, |(jet)|< 0.7) as a function of the transverse energy of the leading “calorimeter jet”.

Compares the number of charged particles within the leading “charged particle jet”, chgjet#1, with the number of charged particles within the leading “calorimeter jet” (JetClu R = 0.7), jet#1.

Nchg(jet#1) versus ET(jet#1)

0

2

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6

8

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12

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

Ave

rave

Nch

gCharged Particles (||<1.0, PT>0.5 GeV/c) JetClu R = 0.7 |(jet)| < 0.7



0

2

4

6

8

10

12

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

Ave

rave

Nch




PYTHIA produces too many charged particles in the

leading “calorimeter jet”!

Nchg(jet#1) and Nchg(charged jet#1)

0

2

4

6

8

10

12

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) or PT(charged jet#1) (GeV)

Ave

rave

Nch



Nchg(jet#1)

Nchg(chgjet#1)

Nchg(jet#1) and Nchg(charged jet#1)

0

2

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6

8

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ET(jet#1) or PT(charged jet#1) (GeV)

Ave

rave

Nch




Nchg(jet#1)

Nchg(chgjet#1)


Rick Field Page 36

The Leading “Jet”The Leading “Jet”

Shows charged particle multiplicity distribution (||<1, PT>0.5 GeV/c) within the leading “charged particle jet” and in the “transverse” region as defined by the leading “charged particle jet” for the range 30 < PT(chgjet#1) < 70 GeV/c compared with PYTHIA Tune A.

Shows charged particle multiplicity distribution (||<1, PT>0.5 GeV/c) within the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 0.7) and in the “transverse” regions as defined by the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 2) for the range 30 < ET(jet#1) < 70 GeV compared with PYTHIA Tune A.

But PYTHIA produces too many charged particles within the leading “jet”!

Charged Particle Multiplicity

0%

5%

10%

15%

20%

25%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

N (charged)

% E

ven

ts


||<1.0 PT>0.5 GeV/c

R = 0.7 30 < PT(chgjet#1) < 70 GeV

"Transverse"

ChgJet#1


Charged Particle Multiplicity

0%

5%

10%

15%

20%

25%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

N (charged)

% E

ven

ts




JetClu R = 0.7 30 < ET(jet#1) < 70 GeV

"Transverse"

Jet#1

PYTHIA Tune A describes the “underlying event”!

PYTHIA Tune A describes the “underlying event”!

But PYTHIA produces too many charged particles within the leading “jet”!


Rick Field Page 37

The Leading “Calorimeter” JetThe Leading “Calorimeter” JetCharged Particle MultiplicityCharged Particle Multiplicity

Shows the Run 2 data on the average number of charged particles (||<1, PT>0.5 GeV, R = 0.7) within the leading “calorimeter jet” (JetClu R = 0.7, |(jet)|< 0.7) as a function of ET(jet#1) compared with PYTHIA Tune A after CDFSIM.


Calorimeter Jet CDFSIM/Generated: Leading Jet Nchg

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

CD

FS

IM/G

ener

ated

PYTHIA Tune A 1.96 TeV JetClu R = 0.7 |(jet)| < 0.7



Correction becomes large for ET(jet#1) > 100 GeV and

depends on ET(jet#1)!

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


0

2

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6

8

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12

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)A

vera

ve N

chg

Charged Particles (||<1.0, PT>0.5 GeV/c) JetClu R = 0.7 |(jet)| < 0.7




0

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14

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)A

vera

ve N

chg

PY Tune A Generated

PY Tune A + CDFSIM

CDF Run 2 Uncorrected Charged Particles (||<1.0, PT>0.5 GeV/c)

JetClu R = 0.7 |(jet)| < 0.7



0

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10

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14

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)A

vera

ve N

chg


JetClu R = 0.7 |(jet)| < 0.7

CDF Preliminarydata corrected

theory uncorrected


Multiply data by the “unfolding function” (i.e. Generated/CDFSIM) determined from PYTHIA Tune A to get “corrected” data.


Rick Field Page 39

The Leading “Jet”The Leading “Jet”

Shows the transverse momentum distribution of charged particles (||<1) within the leading “charged particle jet” compared with PYTHIA Tune A. The plot shows dNchg/dz with z = PT/PT(chgjet#1) for the range 30 < PT(chgjet#1) < 70 GeV/c.

Shows the transverse momentum distribution of charged particles (||<1) within the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 0.7) compared with PYTHIA Tune A. The plot shows dNchg/dz with z = PT/ET(jet#1) for the range 30 < ET(jet#1) < 70 GeV.

PYTHIA produces too many “soft” charged particles within

the leading “jet”!

The integral of F(z) is the average number of charged particles within the

leading “charged particle jet”.

Leading Charged Particle Jet

0.1

1.0

10.0

100.0

0.0 0.2 0.4 0.6 0.8 1.0

z = PT/PT(chgjet#1)

F(z

) =

dN

/dz


R = 0.7 30 < PT(chgjet#1) < 70 GeV

||<1.0 PT>0.5 GeV/c


Leading Calorimeter Jet

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

0.0 0.2 0.4 0.6 0.8 1.0 1.2

z = PT/ET(Jet#1)

F(z

) =

dN

/dz





PYTHIA produces too many “soft” charged particles within

the leading “jet”!


Rick Field Page 40

The Leading The Leading “Calorimeter Jet”“Calorimeter Jet”

Shows average charged PTsum fraction, PTsum/ET(jet#1), and the average charged PTmax fraction, PTmax/ET(jet#1), within the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 0.7) compared with PYTHIA Tune A.

Shows distribution of the charged PTsum fraction, z = PTsum/ET(jet#1), and the distribution of charged PTmax fraction, z = PTmax/ET(jet#1), within the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 0.7) for the range 95 < ET(jet#1) < 130 GeV compared with PYTHIA Tune A.

Charged Fraction within the Leading Jet

0

2

4

6

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

z = PTmax/ET(jet#1) or PTsum/ET(jet#1)

(1/N

) d

N/d

z





PTmax/ET(jet#1)

PTsum/ET(jet#1)

PYTHIA does okay on the charged PTmax fraction!PYTHIA does okay on the charged PTmax fraction!

But PYTHIA does not do well on the charged PTsum fraction!

But PYTHIA does not do as well on the charged PTsum fraction!

PTsum/ET(Jet#1) and PTmax/ET(jet#1)

0.0

0.1

0.2

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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

Ave

rag

e F

ract

ion

JetClu R = 0.7 |(jet)| < 0.7




PTsum(chg)/ET(jet#1)

PTmax(chg)/ET(jet#1)


Rick Field Page 41

The Leading “Calorimeter” JetThe Leading “Calorimeter” JetCharged PTCharged PTsumsum Fraction Fraction

Shows average charged PTsum fraction, PTsum/ET(jet#1), within the leading “calorimeter jet” (JetClu, R = 0.7, |(jet)| < 0.7) compared with PYTHIA Tune A after CDFSIM.

Calorimeter Jet


CDFSIM/Generated: Leading Jet PTsum Fraction

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)

CD

FS

IM/G

ener

ated



JetClu R = 0.7 |(jet)| < 0.7


Very large correction that depends on ET(jet#1)!

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

Leading Jet: Charged PTsum/ET(Jet#1)

0.2

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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)C

har

ged

PT

sum

/ET

(jet

#1)

JetClu R = 0.7 |(jet)| < 0.7




Leading Jet: Charged PTsum/ET(jet#1)

0.2

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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)C

har

ged

PT

sum

/ET

(jet

#1)

PY Tune A Generated

PY Tune A + CDFSIM

CDF Run 2 Uncorrected

JetClu R = 0.7 |(jet)| < 0.7CDF Preliminarydata uncorrected


Leading Jet: Charged PTsum/ET(jet#1)

0.2

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0 25 50 75 100 125 150 175 200 225 250

ET(jet#1) (GeV)C

har

ged

PT

sum

/ET

(jet

#1)

JetClu R = 0.7 |(jet)| < 0.7CDF Preliminary

data correctedtheory uncorrected



Multiply data by the “unfolding function” (i.e. Generated/CDFSIM) determined from PYTHIA Tune A to get “corrected” data.


Rick Field Page 43

Inclusive Cross-SectionInclusive Cross-Section“Correction Factors”“Correction Factors”

Shows PYTHIA Tune A + CDFSIM inclusive cross-section for JetClu (R = 0.7) jets compared with the “true” cross-section where “true” is the PTsum of all hadrons (partons) with PT > 0 in R = 0.7 cone around JetClu.

Inclusive Cross-Section: d/dET

1.0E-07

1.0E-06

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1.0E-04

1.0E-03

1.0E-02

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1.0E+00

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1.0E+02

1.0E+03

0 25 50 75 100 125 150 175 200 225 250

ET (GeV)

d /

dE

T ( b

/Ge

V)

ET(particles in R)

ET(partons in R)

ET(jetclu)


0.1<|DET|<0.7

R = 0.7

Inclusive X-Sec Ratio: (PTsum in R=0.7)/ET(jetclu)

0.5

1.0

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2.0

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0 25 50 75 100 125 150 175 200 225 250

ET(jetclu) (GeV)

Cro

ss

-Se

cti

on

Ra

tio

PYTHIA Tune A 1.96 teV

0.1<|DET|<0.7

Particles

Partons

Measured

“True”

Correction factors!


Rick Field Page 44

Inclusive Cross-SectionInclusive Cross-Section“Jet Energy Scale”“Jet Energy Scale”

Shows PYTHIA Tune A + CDFSIM inclusive cross-section for JetClu (R = 0.7) jets compared with the “true” cross-section where “true” is the PTsum of all hadrons (partons) with PT > 0 in R = 0.7 cone around JetClu.


1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

0 25 50 75 100 125 150 175 200 225 250

ET (GeV)

d /

dE

T ( b

/Ge

V)

ET(particles in R)

ET(partons in R)

ET(jetclu)


0.1<|DET|<0.7

R = 0.7


1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

0 25 50 75 100 125 150 175 200 225 250

ET (GeV)

d /

dE

T ( b

/Ge

V)

ET(particles in R)

ET(partons in R)

ET(jetclu) x 1.12


0.1<|DET|<0.7

R = 0.7

To approximately correct the observed inclusive cross-section back to the

particle level multiply ET(jetclu) by a “scale factor” of 1.12!

Warning!This is only approximate!

The scale shift dependson ET and is only for theinclusive cross-section!


Rick Field Page 45

Summary & ConclusionsSummary & Conclusions


Systematic errors due to initial-state radiation can be estimated by comparing PYTHIA Tune A (more radiation) and PYTHIA Tune B (less radiation).

But it is also important it always compare PYTHIA and HERWIG!The best is to compare all three: PYTHIA (Tune A & B) and

HERWIG.

Proton AntiProton


PT(hard)

Outgoing Parton

Outgoing Parton





Rick Field Page 46

Summary & ConclusionsSummary & Conclusions

The “Underlying Event”

There is excellent agreement between the Run 1 and the Run 2. The “underlying event” is the same in Run 2 as in Run 1 but now we can study the evolution out to much higher energies!

PYTHIA Tune A does a good job of describing the “underlying event” in the Run 2 data as defined by “charged particle jets” and as defined by “calorimeter jets”. HERWIG Run 2 comparisons will be coming soon!

Lots more CDF Run 2 data to come including MAX/MIN “transverse” and MAX/MIN “cones”.

Proton AntiProton

PT(hard)

Outgoing Parton

Outgoing Parton




Also see Mario’s Run 2 “energy flow” analysis!


Rick Field Page 47

MichelangeloMichelangeloA Few Thoughts

The determination of the top mass will unfortunately have to rely on some MC input. This is true even in absence of backgrounds. It is therefore useful to start right away separating the background-related issues with the signal-related ones. Let us start from the signal.

Let us assume there is no background contamination and we isolated a pure sample of t-tbar events. The key experimental systematics will then be related to

• light jet energy scale

• b jet energy scale

• initial/final-state radiation effects

• acceptance/event selection biases Information on the corrections which have to be applied to the data is obtained through

control samples (e.g. gamma+jet or Z+jets for the e-scale of light jets). However more work is required to transport these energy corrections to the physically different environment of light jets in a t-tbar final state. The "porting" procedure, which is driven by MC modeling, needs to be validated, and its systematics established.


Rick Field Page 48


Reasons why the porting of e-scale corrections is non-trivial (namely requires model-dependent corrections) include:• light jets in top events arise from W decays. Their properties (energy, multiplicity,

fragmentation function) are fixed in the W rest frame. When the W is boosted, the jets' ET will change, but properties like multiplicity and fragmentation function won't (up to detector effects). Therefore a 20 GeV or an 80 GeV light jet in top decays behave as a 40 GeV jet, boosted to 20 or 80 GeV, rather than as a 20 or 80 GeV jet produced as a recoil to a gamma or a Z.

• a similar comment applies to b jets, for the same reason.• light jets in top decays are dominated by quarks, while in any other control sample there

will typically be gluons as well; so the features of the jets are different. Studies which should be performed to validate the procedures should include:

• a study of the fragmentation function (or even more inlcusive observables, such as jet shapes, jet energy profiles) for light jets and b-tagged jets in top-rich samples.

• a study of frag-function (or more inclusive observables, as above) in the e-cale-fixing contorl samples (gamma+jet, Z+jet).


Rick Field Page 49


The exercise can start even before the data have statistics large enough. For example, it would be good to compare the properties of the jets in the two MC samples (in other words, to compare the MC predictions for the structure of a 40 GeV jet recoiling against a gamma, and a 40 gev jet form top decay), as well as simulating how large the MC predicts the corrections will have to be. If the differences/corrections are small, we can gain confidence that this won't be a problem (the same exercise will have to be repeated using PYTHIA and HERWIG, at least). If the differences/corrections are large, we will need a careful planning of MC-validation measurements.

Information about the description of the ISR and FSR has to be brought into the game. All studies done on the structure of the UE in Z/W events (see work done by Rick) have to be brought into a t-mass measurement perspective. Assuming that most extra jet form ISR in top events will come at large rapidity, a possible first observable could be the rapidity distribution of soft jets in t-tbar events. In order to test the ISR performance of the MC’s on control samples which share some of the physics of t-tbar events, I would propose the following:

• large-mass DY events (take events with DY masses as close as possible to 2 mtop; the stastistics is lousy, if non 0. one should then go to masses as large as possible, and monitor the jet activity as a function of DY mass).


Rick Field Page 50


• large mass dijets: take events with two high-pt, central jets (within eta<1), and study the extra-jet activity as a function of the dijet mass. Extra jets well separated from the leading 2 jets will most likely arise from ISR (the MC can help deciding how to define the extra jets to optimize this requirement; mayve a cut in deltaR is enough, maybe a request eta>2 is better). Tests done in the region of dijet mass close to 400 gev will provide strong constraints on the mc description of isr in t-tbar events.

Other issues:• Calibrating the light jets "on site" using the W mass constraint on an event-by-event basis is useful,

but is not an unambiguous procedure. Since we have two jets, there is an infinite number of ways in which the energy of the two jets can be independently rescaled, all leading to the W mass, but each leading to a different value fo the W momentum (which is the ingredient entering in the top mass fit). Whatever the recipe to calibrate light jets, the impact of this ambiguity should be established (the MC, however inaccurate, should be enough to assess this systematics).

• In W decays we'll typically have a 3rd jet. Whether or not this appears as an independent jet after the W boost requires some study. Any algorithm aimed at establishing rules for the acceptance or rejection of extra jets (or for the dynamical rescaling of the jet cones, to try absorb the extra jet) ultimately requires validation, and to first approximation requires an evaluation of the systematics based on the MC.


Rick Field Page 51


In general, I would expect the MC to describe well these decays, since the physics was constrained by the 3 jet decays at LEP. It is important to verify whether the MC has matrix element corrections. PYTHIA, as well as the most recent versions of HERWIG, have them in,

Background issues: Here the problem is mostly to understand how the background affects the MC validation procedures which use the top-enhanced sample.

The issue can be tackled by ensuring that the MC's for the background correctly reproduce the jet properties in background-dominated samples. So the above analyses should be repeated using, for example, non-b tagged W+3/4 jets final states (or,even better, Z+3/4 jets). The statistics is larger, so I would look at fragmentation functions, jet shapes, inter-jet radiation patterns.

The whole process will take a long time, and will need coordination with the QCD group. The question "what do we do if the MC's don't describe the data or "ok, the MC seems fine, what's next" can only be addressed once we have made a first pass.

qcd monte-carlo generators in run 2 at cdf

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