lecture 1: university of chicago july 7, 2006 rick field – florida/cdfpage 1 university of chicago...

Lecture 1: University of Chicago July 7, 2006

Rick Field – Florida/CDF

University of ChicagoUniversity of Chicago

Calorimeter Jet

Rick FieldUniversity of Florida

CDF Run 2

Enrico Fermi Institute, University of Chicago

Lecture 1: Toward an Understanding of Hadronization

From Feynman-Field to the Tevatron

Charged Particle Jet



Toward and Understanding of Toward and Understanding of HadronizationHadronization

From 7 GeV/c 0’s to 600 GeV/c Jets.

Some things we have learned about quark and gluon jets at CDF.

Jet algorithms and the “jet” cross section at CDF.

From Feynman-Field to the Tevatron

Feynman and Field

Proton AntiProton

PT(hard)

Outgoing Parton

Outgoing Parton

Underlying Event Underlying Event

Initial-State Radiation

Final-State Radiation

1st hat!



“Feynman-Field Jet Model”

The FeynmanThe Feynman-Field -Field DaysDays

FF1: “Quark Elastic Scattering as a Source of High Transverse Momentum Mesons”, R. D. Field and R. P. Feynman, Phys. Rev. D15, 2590-2616 (1977).

FFF1: “Correlations Among Particles and Jets Produced with Large Transverse Momenta”, R. P. Feynman, R. D. Field and G. C. Fox, Nucl. Phys. B128, 1-65 (1977).

FF2: “A Parameterization of the properties of Quark Jets”, R. D. Field and R. P. Feynman, Nucl. Phys. B136, 1-76 (1978).

F1: “Can Existing High Transverse Momentum Hadron Experiments be Interpreted by Contemporary Quantum Chromodynamics Ideas?”, R. D. Field, Phys. Rev. Letters 40, 997-1000 (1978).

FFF2: “A Quantum Chromodynamic Approach for the Large Transverse Momentum Production of Particles and Jets”, R. P. Feynman, R. D. Field and G. C. Fox, Phys. Rev. D18, 3320-3343 (1978).

1973-1983

FW1: “A QCD Model for e+e- Annihilation”, R. D. Field and S. Wolfram, Nucl. Phys. B213, 65-84 (1983).

My 1st graduate student!



Before Feynman-FieldBefore Feynman-Field

Rick Field 1968



Before Feynman-FieldBefore Feynman-Field

Rick & Jimmie1968

Rick & Jimmie1970 Rick & Jimmie

1972 (pregnant!)

Rick & Jimmie at CALTECH 1973



Hadron-Hadron CollisionsHadron-Hadron Collisions

What happens when two hadrons collide at high energy?

Most of the time the hadrons 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.

Occasionally there will be a large transverse momentum meson. Question: Where did it come from?

We assumed it came from quark-quark elastic scattering, but we did not know how to calculate it!

Hadron Hadron ???

Hadron Hadron

“Soft” Collision (no large transverse momentum)

Hadron Hadron

high PT meson

Parton-Parton Scattering

Outgoing Parton

Outgoing Parton

FF1 1977 (preQCD)

Feynman quote from FF1“The model we shall choose is not a popular one,

so that we will not duplicate too much of thework of others who are similarly analyzing various models (e.g. constituent interchange

model, multiperipheral models, etc.). We shall assume that the high PT particles arise from direct hard collisions between constituent quarks in the incoming particles, which

fragment or cascade down into several hadrons.”

“Black-Box Model”



QuarkQuark--Quark BlackQuark Black--Box ModelBox Model

FF1 1977 (preQCD)Quark Distribution Functionsdetermined from deep-inelastic

lepton-hadron collisions

Quark Fragmentation Functionsdetermined from e+e- annihilationsQuark-Quark Cross-Section

Unknown! Deteremined fromhadron-hadron collisions.

No gluons!

Feynman quote from FF1“Because of the incomplete knowledge of

our functions some things can be predicted with more certainty than others. Those experimental results that are not well

predicted can be “used up” to determine these functions in greater detail to permit better predictions of further experiments. Our papers will be a bit long because we wish to discuss this interplay in detail.”



Quark-Quark Black-Box ModelQuark-Quark Black-Box Model

FF1 1977 (preQCD)Predict

particle ratios

Predictincrease with increasing

CM energy W

Predictoverall event topology

(FFF1 paper 1977)

“Beam-Beam Remnants”

7 GeV/c 0’s!



Telagram from FeynmanTelagram from Feynman

July 1976

SAW CRONIN AM NOW CONVINCED WERE RIGHT TRACK QUICK WRITEFEYNMAN



Letter from FeynmanLetter from Feynman

July 1976



Letter from Feynman Page 1Letter from Feynman Page 1

Spelling?



Letter from Feynman Page 3Letter from Feynman Page 3It is fun!

Onward!



Feynman Talk at Coral GablesFeynman Talk at Coral Gables (December 1976)(December 1976)

“Feynman-Field Jet Model”

1st transparency Last transparency



QCD Approach: Quarks & GluonsQCD Approach: Quarks & Gluons

FFF2 1978

Parton Distribution FunctionsQ2 dependence predicted from

QCD

Quark & Gluon Fragmentation Functions

Q2 dependence predicted from QCD

Quark & Gluon Cross-SectionsCalculated from QCD

Feynman quote from FFF2“We investigate whether the present

experimental behavior of mesons with large transverse momentum in hadron-hadron

collisions is consistent with the theory of quantum-chromodynamics (QCD) with

asymptotic freedom, at least as the theory is now partially understood.”



High PHigh PTT Jets Jets

30 GeV/c!

Predictlarge “jet”

cross-section

Feynman, Field, & Fox (1978)CDF (2006)

600 GeV/c Jets!Feynman quote from FFF

“At the time of this writing, there is still no sharp quantitative test of QCD.

An important test will come in connection with the phenomena of high PT discussed here.”



A Parameterization of A Parameterization of the Properties of Jetsthe Properties of Jets

Assumed that jets could be analyzed on a “recursive” principle.

Field-Feynman 1978

Original quark with flavor “a” and momentum P0

bb pair

(ba)

Let f()d be the probability that the rank 1 meson leaves fractional momentum to the remaining cascade, leaving quark “b” with momentum P1 = 1P0.

cc pair

(cb) Primary Mesons

Assume that the mesons originating from quark “b” are distributed in presisely the same way as the mesons which came from quark a (i.e. same function f()), leaving quark “c” with momentum P2 = 2P1 = 21P0.

Add in flavor dependence by letting u = probabliity of producing u-ubar pair, d = probability of producing d-dbar pair, etc.

Let F(z)dz be the probability of finding a meson (independent of rank) with fractional mementum z of the original quark “a” within the jet.

Rank 2

continue

Calculate F(z) from f() and i!

(bk) (ka)

Rank 1

Secondary Mesons(after decay)



Feynman-Field Jet ModelFeynman-Field Jet ModelR. P. Feynman

ISMD, Kaysersberg, France, June 12, 1977

Feynman quote from FF2“The predictions of the model are reasonable

enough physically that we expect it may be close enough to reality to be useful in

designing future experiments and to serve as a reasonable approximation to compare

to data. We do not think of the model as a sound physical theory, ....”



Monte-Carlo SimulationMonte-Carlo Simulationof Hadron-Hadron Collisionsof Hadron-Hadron Collisions

FF1-FFF1 (1977) “Black-Box” Model

F1-FFF2 (1978) QCD Approach

FF2 (1978) Monte-Carlo

simulation of “jets”

FFFW “FieldJet” (1980) QCD “leading-log order” simulation

of hadron-hadron collisions

ISAJET(“FF” Fragmentation)

HERWIG(“FW” Fragmentation)

PYTHIAtoday

“FF” or “FW” Fragmentationthe past

tomorrow SHERPA PYTHIA 6.3



QCD Monte-Carlo Models:QCD Monte-Carlo Models:High Transverse Momentum JetsHigh Transverse Momentum Jets

Start with the perturbative 2-to-2 (or sometimes 2-to-3) parton-parton scattering and add initial and final-state gluon radiation (in the leading log approximation or modified leading log approximation).

Hard Scattering

PT(hard)

Outgoing Parton

Outgoing Parton



Hard Scattering

PT(hard)

Outgoing Parton

Outgoing Parton



Proton AntiProton


Proton AntiProton


“Hard Scattering” Component “Jet”

“Jet”

“Underlying Event”

The “underlying event” consists of the “beam-beam remnants” and from particles arising from soft or semi-soft multiple parton interactions (MPI).

Of course the outgoing colored partons fragment into hadron “jet” and inevitably “underlying event” observables receive contributions from initial and final-state radiation.

“Jet”

The “underlying event” is an unavoidable background to most collider observables and having good understand of it leads to

more precise collider measurements!



Monte-Carlo SimulationMonte-Carlo Simulationof Quark and Gluon Jetsof Quark and Gluon Jets

ISAJET: Evolve the parton-shower from Q2 (virtual photon invariant mass) to Qmin ~ 5 GeV. Use a complicated fragmentation model to evolve from Qmin to outgoing hadrons.

Q2

Field-Feynman

hadrons

5 GeV 1 GeV 200 MeV

HERWIG: Evolve the parton-shower from Q2 (virtual photon invariant mass) to Qmin ~ 1 GeV. Form color singlet clusters which “decay” into hadrons according to 2-particle phase space.

MLLA: Evolve the parton-shower from Q2 (virtual photon invariant mass) to Qmin ~ 230 MeV. Assume that the charged particles behave the same as the partons with Nchg/Nparton = 0.56!

CDF Distribution of Particles in Jets MLLA Curve!

= ln(Ejet/pparticle)



Distribution of Particles in JetsDistribution of Particles in Jets

Ratio of charged hadron multiplicities in gluon and quark jets agrees with NNLLA

Gluon-Quark Ratio = 1.6 0.2

Momentum distribution of charged hadrons in jets well described by MLLA!

Dijet mass range 80-600 GeV Cutoff Qeff = 230 40 MeV

Ncharged-hadrons/Npartons = 0.56 0.10

CDF Run 1 Analysis

Rat

io =

Ng-

jet /

Nq-

jet

Q = Ejet cone

Both PYTHIA and HERWIG predict a Gluon-Quark Ratio that is smaller than the data!



Charged Multiplicity Charged Multiplicity in Quark and Gluon Jetsin Quark and Gluon Jets

CDF Run 1 data on the average charged particle multiplicities in gluon and quark jets versus Q = Ejet × cone compared with NLLA, PYTHIA, and HERWIG.

HERWIG and PYTHIA correctly predict the charged multiplicity for gluon jets.

Both HERWIG and PYTHIA over-estimate the charged multiplicity in quark jets by ~30%!

CDF Run 1 Analysis



Distribution of Particles Distribution of Particles in Quark and Gluon Jetsin Quark and Gluon Jets

Momentum distribution of charged particles in gluon jets. HERWIG 5.6 predictions are in a good agreement with CDF data. PYTHIA 6.115 produces slightly more particles in the region around the peak of distribution.

x = 0.37 0.14 0.05 0.02 0.007

Momentum distribution of charged particles in quark jets. Both HERWIG and PYTHIA produce more particles in the central region of distribution.

pchg = 2 GeV/c

Both PYTHIA and HERWIG predict more charged particles

than the data for quark jets!

CDF Run 1 Analysis



Evolution of Charged JetsEvolution of Charged Jets“Underlying Event”“Underlying Event”

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.

Charged Jet #1Direction

“Toward”


“Away”

-1 +1

2

0

Leading Jet

Toward Region

Transverse Region

Transverse Region

Away Region

Away Region

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

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

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

CDF Run 1 Analysis



Charged Particle Jet #1 Direction

“Toward”


“Away”

"Transverse" Charged Particle Density: dN/dd

0.00

0.25

0.50

0.75

1.00

1.25

0 5 10 15 20 25 30 35 40 45 50

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

ran

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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


0.00

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0.75

1.00

1.25

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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

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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

CDF Run 2 Preliminary

PYTHIA Tune A

||<1.0 PT>0.5 GeV/c

CDF Preliminarydata uncorrectedtheory corrected

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).



Charged Multiplicity Charged Multiplicity in Charged Particle Jetsin Charged Particle Jets

Plot shows the average number of charged particles (pT > 0.5 GeV, || < 1) within the leading charged particle jet (R = 0.7) as a function of the PT of the leading charged jet. The solid (open) points are Min-Bias (JET20) data. The errors on the (uncorrected) data include both statistical and correlated systematic uncertainties. The QCD “hard scattering” theory curves (Herwig 5.9, Isajet 7.32, Pythia 6.115) are corrected for the track finding efficiency.

Nchg (jet#1) versus PT(charged jet#1)

0

2

4

6

8

10

12

0 5 10 15 20 25 30 35 40 45 50

PT(charged jet#1) (GeV)

Herwig Isajet Pythia 6.115 CDF Min-Bias CDF JET20

1.8 TeV |eta|<1.0 PT>0.5 GeV

<Nchg> (Jet#1, R=0.7)

CDFdata uncorrectedtheory corrected

PYTHIA predict more charged particles than the

data for charged jets!

Includes charged particles from the “underlying event”!

CDF Run 1 Analysis



Charged Multiplicity Charged Multiplicity in Charged Particle Jetsin Charged Particle Jets

CDF Run 1 data on the multiplicity distribution of charged particles (pT > 0.5 GeV and || < 1) within chgjet#1 (leading charged jet) for PT(chgjet#1) > 5 and 30 GeV compared with the QCD “hard scattering” Monte-Carlo predictions of HERWIG 5.9, ISAJET 7.32, and PYTHIA 6.115. Plot shows the percentage of events in which the leading charged jet (R = 0.7) contains Nchg charged particles.

Jet#1 Charged Multiplicity Distribution

0%

10%

20%

30%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Nchg

% with Nchg

1.8 TeV |eta|<1.0 PT>0.5 GeV


PT(jet#1) > 5 GeV

PT(jet#1) > 30 GeV

HerwigIsajet

Pythia

CDF Run 1 Analysis



Rick Field – Florida/CDF Page 29

Run 1 Fragmentation FunctionRun 1 Fragmentation Function

CDF Run 1 data on the momentum distribution of charged particles (pT > 0.5 GeV and || < 1) within chgjet#1 (leading charged jet). The points are the charged number density, F(z) = dNchg/dz, where z = pchg/P(chgjet#1) is the ratio of the charged particle momentum to the charged momentum of chgjet#1. The integral of F(z) is the average number of particles within chgjet#1.

Charged Momentum Distribution Jet#1

0.1

1.0

10.0

100.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

z = p(charged)/P(charged jet#1)

Density F(z)=dNchg/dz

1.8 TeV |eta|<1.0 PT>0.5 GeV

PT(jet#1) > 30 GeV<NchgJet#1> = 8.0

PT(jet#1) > 2 GeV<NchgJet#1> = 2.5

CDFdata uncorrectedPT(jet#1) > 5 GeV

<NchgJet#1> = 4.0


CDF Run 1 Analysis




CDF Run 1 data from on the momentum distribution of charged particles (pT > 0.5 GeV and || < 1) within chgjet#1 (leading charged jet) for PT(chgjet#1) > 5 GeV compared with the QCD “hard scattering” Monte-Carlo predictions of HERWIG, ISAJET, and PYTHIA. The points are the charged number density, F(z) = dNchg/dz, where z = pchg/P(chgjet#1) is the ratio of the charged particle momentum to the charged momentum of chgjet#1.


0.1

1.0

10.0

100.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9


Herwig

Isajet

Pythia 6.115

CDF Min-Bias


1.8 TeV |eta|<1.0 PT>0.5 GeV


PT(jet#1) > 5 GeV

CDF Run 1 Analysis

PYTHIA does not agree at high z!




Data from Fig. 3.8 on the momentum distribution of charged particles (pT > 0.5 GeV and || < 1) within chgjet#1 (leading charged jet) for PT(chgjet#1) > 30 GeV compared with the QCD “hard scattering” Monte-Carlo predictions of HERWIG, ISAJET, and PYTHIA. The points are the charged number density, F(z) =dNchg/dz, where z = pchg/P(chgjet#1) is the ratio of the charged particle momentum to the charged momentum of chgjet#1.


0.1

1.0

10.0

100.0

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9


Herwig

Isajet

Pythia 6.115

CDF JET20


1.8 TeV |eta|<1.0 PT>0.5 GeV


PT(jet#1) > 30 GeV

CDF Run 1 Analysis

PYTHIA does not agree at high z!



Jet #1 Direction


“Toward”

“Away”

“Toward-Side” Jet

“Away-Side” Jet

The “Transverse” RegionsThe “Transverse” Regionsas defined by the Leading Jetas defined by the Leading Jet

Look at charged particle correlations in the azimuthal angle relative to the leading calorimeter jet (JetClu R = 0.7, || < 2).

Define || < 60o as “Toward”, 60o < - < 120o and 60o < < 120o as “Transverse 1” and “Transverse 2”, and || > 120o as “Away”. Each of the two “transverse” regions have area = 2x60o = 4/6. The overall “transverse” region is the sum of the two transverse regions ( = 2x120o = 4/3).

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

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

Jet #1 Direction

“Toward”

“Trans 1” “Trans 2”

“Away”

-1 +1

2

0

Leading Jet

Toward Region

Transverse Region 1

Transverse Region 2

Away Region

Away Region

Look at the charged particle density in the “transverse” region!CDF Run 2 Analysis



Charged Particle Density Charged Particle Density Dependence Dependence

Look at the “transverse” region as defined by the leading jet (JetClu R = 0.7, || < 2) or by the leading two jets (JetClu R = 0.7, || < 2). “Back-to-Back” events are selected to have at least two jets with Jet#1 and Jet#2 nearly “back-to-back” (12 > 150o) with almost equal transverse energies (ET(jet#2)/ET(jet#1) > 0.8) and with ET(jet#3) < 15 GeV.

Charged Particle Density: dN/dd

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

30 < ET(jet#1) < 70 GeV

"Transverse" Region

Jet#1

Jet #1 Direction

“Toward”


“Away”

Jet #1 Direction

“Toward”


“Away”

Jet #2 Direction

Shows the dependence of the charged particle density, dNchg/dd, for charged particles in the range pT > 0.5 GeV/c and || < 1 relative to jet#1 (rotated to 270o) for 30 < ET(jet#1) < 70 GeV for “Leading Jet” and “Back-to-Back” events.


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Back-to-Back

Leading Jet

Min-Bias



30 < ET(jet#1) < 70 GeV

"Transverse" Region

Jet#1

Refer to this as a “Leading Jet” event

Refer to this as a “Back-to-Back” event

Su

bset



““Transverse” Charge DensityTransverse” Charge Density PYTHIA Tune A vs HERWIG PYTHIA Tune A vs HERWIG

Jet #1 Direction

“Toward”


“Away”

Jet #1 Direction

“Toward”


“Away”

Jet #2 Direction

Shows the average charged particle density, dNchg/dd, in the “transverse” region (pT > 0.5 GeV/c, || < 1) versus ET(jet#1) for “Leading Jet” and “Back-to-Back” events.

“Leading Jet”

“Back-to-Back”

"AVE Transverse" Charge Density: dN/dd

0.0

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1.0

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ET(jet#1) (GeV)

"Tra

ns

ve

rse

" C

ha

rge

De

ns

ity

CDF Preliminarydata uncorrectedtheory + CDFSIM

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

Leading Jet

Back-to-Back

PY Tune A

HW

Now look in detail at “back-to-back” events in the region 30 < ET(jet#1) < 70 GeV!

Compares the (uncorrected) data with PYTHIA Tune A and HERWIG after CDFSIM.



Charged Particle DensityCharged Particle DensityPYTHIA Tune A vs HERWIG PYTHIA Tune A vs HERWIG


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PY Tune A


30 < ET(jet#1) < 70 GeVCharged Particles (||<1.0, PT>0.5 GeV/c)

"Transverse" Region

Jet#1

Data - Theory: Charged Particle Density dN/dd

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Da

ta -

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Back-to-Back30 < ET(jet#1) < 70 GeV

PYTHIA Tune A

Jet#1

"Transverse" Region


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HERWIG


30 < ET(jet#1) < 70 GeVCharged Particles (||<1.0, PT>0.5 GeV/c)

"Transverse" Region

Jet#1

Data - Theory: Charged Particle Density dN/dd

-1.0

-0.5

0.0

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1.0

0 30 60 90 120 150 180 210 240 270 300 330 360

(degrees)

Da

ta -

Th

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Back-to-Back30 < ET(jet#1) < 70 GeV

HERWIG

Jet#1

"Transverse" Region

HERWIG (without multiple parton interactions) produces too few charged

particles in the “transverse” region for 30 < ET(jet#1) < 70 GeV!

PYTHIA produces too many particle in the “away-side” jet!



Jet AlgorithmsJet Algorithms Clustering algorithms are used to combine calorimeter towers or charged particles into “jets”

in order to study the event topology and to compare with the QCD Monte-Carlo Models.

We do not detect partons! The outgoing partons fragment into hadrons before they travel a distance of about the size of the proton. At long distances the partons manifest themselves as “jets”. The “underlying event” can also form “jets”. Most “jets” are a mixture of particles arising from the “hard” outgoing partons and the “underlying event”.

Every “jet” algorithms correspond to a different observable and different algorithms give different results.

Since we measure hadrons every observable is infrared and collinear safe. There are no divergences at the hadron level!

Studying the difference between the algorithms teaches us about the event structure.



Jet Corrections & ExtrapolationsJet Corrections & Extrapolations

Proton AntiProton

PT(hard)

Outgoing Parton

Outgoing Parton




Calorimeter Level Jets → Hadron Level Jets: We measure “jets” at the “hadron level” in the calorimeter. We certainly want to correct the “jets” for the detector resolution

and efficiency. Also, we must correct the “jets” for “pile-up”. Must correct what we measure back to the true “hadron level” (i.e.

particle level) observable!Particle Level Jets (with the “underlying event” removed):

Do we want to make further model dependent corrections? Do we want to try and subtract the “underlying event” from the

observed “particle level” jets. This cannot really be done, but if you trust the Monte-Carlo

modeling of the “underlying event” you can do it by using the Monte-Carlo models (use PYTHIA Tune A).

This is no longer an observable, it is a model dependent extrapolation!

Hadron Level Jets → Parton Level Jets: Do we want to use the data to try and extrapolate back to the

parton level? What parton level, PYTHIA (Leading Log) or fixed order NLO?

This also cannot really be done, but again if you trust the Monte-Carlo models you can try and do it by using the Monte-Carlo models (use PYTHIA Tune A) including ISR and FSR.

Cannot extrapolate the data to fixed order NLO!

I do not believe we should extrapolatethe data to the parton level! We should

publish what we measure (i.e. hadron levelwith the “underlying event”)!

To compare with theory we should “extrapolate” the parton level to the

hadron level (i.e. add hadronization and the “underlying event” to the parton level)!

PYTHIA, HERWIG, MC@NLO

Had

ron

← P

arton

Useless without a model of hadronization!

Next-to-leading order parton level calculation

0, 1, 2, or 3 partons!



Good and Bad AlgorithmsGood and Bad Algorithms

In order to correct what we see in the calorimeter back to the hadron level we must use an algorithm that can be defined at both the calorimeter and particle level.

If you insist on extrapolating the data to the parton level then it is better to use an algorithm that is well defined at the parton level (i.e. infrared and collinear safe at the parton level).

Infrared Safety (Parton Level) Soft parton emission changes jet multiplicity

below threshold(no jets)

above threshold(1 jet)

Collinear Safety (Parton Level)

Calorimeter Jet

Particle Jet

If you hadronize the parton level and add the “underlying event” (i.e. PYTHIA, HERWIG, MC@NLO) then you do not care if the algorithm is infrared and collinear safe at the parton level. You can predict any hadron level observable!



Four Jet AlgorithmsFour Jet Algorithms

JetClu is bad because the algorithm cannot be defined at the particle level.

Bad

The MidPoint and Modified MidPoint (i.e. Search Cone) algorithms are not infrared and collinear safe at the parton level.

Towers not included in a jet (i.e. “dark towers”)!



KKTT Algorithm Algorithm

Proton AntiProton

PT(hard)

Outgoing Parton

Outgoing Parton




kT Algorithm: Cluster together calorimeter towers by their kT proximity. Infrared and collinear safe at all orders of pQCD. No splitting and merging. No ad hoc Rsep parameter necessary to compare with parton level. Every parton, particle, or tower is assigned to a “jet”. No biases from seed towers. Favored algorithm in e+e- annihilations!

For each precluster, calculate 2

,iTi pd

For each pair of preculsters, calculate

2

222

,2

,

)()(),min(

D

yyppd jiji

jTiTij

Find the minimum of all di and dij.

Move i to list of jets

no

yes

Begin

End

Minumum is dij?

Any Preclusters

left?

no

Merge i and j

yes

KT Algorithm

Only towers with ET > 0.5 GeV are shown

Raw Jet ET = 533 GeVRaw Jet ET = 618 GeV

Will the KT algorithm be effective in the collider

environment where there is an “underlying event”?

CDF Run 2



KKTT Inclusive Jet Cross Section Inclusive Jet Cross Section

KT Algorithm (D = 0.7) Data corrected to the hadron level L = 385 pb-1

0.1 < |yjet| < 0.7 Compared with NLO QCD (JetRad)

corrected to the hadron level.

Sensitive to UE + hadronization effects for PT < 300 GeV/c!



Search Cone Search Cone Inclusive Jet Cross SectionInclusive Jet Cross Section

Modified MidPoint Cone Algorithm (R = 0.7, fmerge = 0.75)

Data corrected to the hadron level and the parton level

L = 1.04 fb-1

0.1 < |yjet| < 0.7

Compared with NLO QCD (JetRad, Rsep = 1.3)

Sensitive to UE + hadronization effects for PT < 200 GeV/c!



Hadronization and Hadronization and “Underlying Event” Corrections“Underlying Event” Corrections

Compare the hadronization and “underlying event” corrections for the KT algorithm (D = 0.7) and the MidPoint algorithm (R = 0.7)!

MidPoint Cone Algorithm (R = 0.7)

The KT algorithm is slightly more sensitive to the “underlying event”!

We see that the KT algorithm (D = 0.7) is slightly more sensitive to the underlying event than the cone algorithm (R = 0.7), but with a good model of the “underlying event” both cross sections can be measured at the Tevatrun!

Note that DØ does not make any corrections for hadronizationor the “underlying event”!?



Summary and ConclusionsSummary and Conclusions Neither HERWIG or PYTHIA describe

precisely the distribution charged particles in quark and gluon jets at the Tevatron!

Particle Jet

To learn about the fragmentation function at large z we should compare the inclusive “jet” cross-section to the inclusive charged particle cross section!

We have events with 600 GeV “jets” so we must have events with 300 GeV/c charged particles!

Was this measured in Run 1?

A lot of work has been done in comparing to analytic MLLA calculations (Korytov and students), but more work needs to be done in improving the fragmentation models in HERWIG and PYTHIA!

I wish I could show you the following: CDF measured fragmentation functions at different Q2

compared with PYTHIA and HERWIG. The kT distribution of charged particles within “jets”

compared with PYTHIA and HERWIG. The ratio of the inclusive charged particle cross-section to

the inclusive “jet” cross-section compared with PYTHIA and HERWIG.

Charged Particle PT Distribution

1,000

10,000

100,000

1,000,000

10,000,000

100,000,000

0 10 20 30 40 50 60 70 80 90 100

Charged Particle PT (GeV/c)N

um

ber

in

1 G

eV/c

Bin

CDF Run 2 Pre-PreliminaryJet 100 Trigger

Charged Particles (||<1.0)

1.96 TeV

In 1 fb-1 we have thousands of charged tracks with pT > 100 GeV/c!

Sergo is “blessing” this today in the QCD group!

Charged Particle kT Distribution in Jets

Shape Comparison Only

lecture 1: university of chicago july 7, 2006 rick field – florida/cdfpage 1 university of chicago...

Documents

quarkquark elastic scattering

properties of quark

hadronhadron collisionswhat

large transverse momenta

high pt particles

gevc jets

quark crosssectionunknown

e e annihilationsquark