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Inclusive Jet Cross Section Jet Energy Corrections. Anwar Ahmad Bhatti DOE Meeting December 2, 2004. QCD and Jet Physics. All production processes are “QCD related” Optimal understanding is basic for all analyses: - Main parameters (ex: gluon PDFs in high x)

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Inclusive jet cross section jet energy corrections
Inclusive Jet Cross SectionJet Energy Corrections

Anwar Ahmad Bhatti

DOE Meeting

December 2, 2004

Qcd and jet physics
QCD and Jet Physics

  • All production processes are “QCD related”

    Optimal understanding is basic for all analyses:

    - Main parameters (ex: gluon PDFs in high x)

    - Non perturbative regime (ex: underlying event studies)

    - Studies of specific processes where QCD is important

    Probe higher energy scales:

    - Precise test of perturbative QCD at NLO

    - Look for deviations due to new physics

    Run I studies were at 10-20%. Aim to improve them to 5%

    a) Requires better understanding of relation between experimental measurements and theory (NLO QCD) predictions, kinematic variables clustering, etc.

    b) Non-perturbative corrections, underlying events, hadronization

    c) Jet energy scale, but improvements limited by detector, jet fragmentation

Inclusive jet cross section
Inclusive Jet Cross Section

  • In Run I, inclusive jet cross section had an excess at high jet Et.

  • The gluon distribution at high x, driven by D0 forward jet data, has indeed increased in recent PDF fits (CTEQ6, CTEQ6.1) leading to better agreement with the CDF inclusive jet cross section measurements.

  • In Run II, the increase in √s and the large integrated luminosity will increase the kinematic range to 600 GeV.

  • The RunII inclusive jet cross section are consistent with the NLO QCD prediction CTEQ 6.1. The band represents 5% uncertainty in jet energy scale.

Ratio of data over theory
Ratio of Data over Theory

Run I with CTEQ6.1


  • The data are in good agreement with the NLO CTEQ predictions within the theoretical and experimental uncertainty bands.

  • There is some indications of the data being below the theory at low ET and somewhat above at higher ET, but overall the agreement is good!

Midpoint cone clustering algorithm
MidPoint Cone Clustering Algorithm

Add seed at middle of two clusters to remove infra red/collinear singularities

Less detector dependence at clustering stage.

Different/better kinematic variables (4-vector and rapidity, instead of Et and η )

New method to correct for energy scale and smearing


Data Corrected to hadrons

Data Corrected to partons

Pt of the Jet (GeV)

Kt clustering parton shower motivated
Kt Clustering (Parton shower motivated)

Same trend in PYTHIA and HERWIG

The comparison is not fair as data is not corrected for underlying event .

Current status of inclusive jet
Current Status of Inclusive Jet

  • 350 pb-1 data

  • Group decision to “bless” once the results are final i.e. energy scale uncertainty is reduced below or same as Run I (~3%).

  • Calorimeter calibration/ Run II corrections in good shape but need some more work.

  • Working on a different method to correct the measured spectrum. Compare and study biases.

  • Better understanding of new clustering algorithm. Still evolving as data has configurations where current MidPoint clustering fails.

  • The papers ready in next 4-6 months.

Jet energy scale and top mass
Jet Energy Scale and Top Mass

Dynamical Likelihood Method

b-tagging, lepton+4 jets, 162 pb-1

22 events 4 background

Generic jet corrections at cdf
Generic Jet Corrections at CDF

  • Jet Energy and Resolution Group

    (Anwar, Florencia Canelli, Tommaso Dorigo)

    Charge includes

    a) generic, b-jet corrections

    b ) improving di-jet mass resolution geared for dijet/b\bar b resonances.

  • The jet energy scale is determined by convoluting the individual particle response with particle spectrum. It requires good understanding of

    a) single particle response, measured in data (test beam) and used by simulation

    b) particle spectrum in the jets.

    Use photon-jet and Z-jet as a cross check.

  • Steps in the corrections

    1. η-dependent corrections, scale all jets in the event to equivalent jets in central calorimeter. (Ken Hatakeyama will talk about it.)

    2. Subtract energy from additional p\bar p interactions

    3. Correct calorimeter-level jets energy to hadron-level jets for central calorimeter non-linearity.

    4. Subtract “underlying event”

    5. Add energy outside the clustering cone to recover parton energy.

Calorimeter response to jets
Calorimeter Response to Jets

Pythia PtMin=40

Jet HadPt

Particle P

Hadron Momentum (GeV)

  • Calorimeter Response to jet is convolution of momentum of hadrons with cal response to individual particles.

  • Pt spectrum of particles in jets varies slowly with Jet Pt.

  • EM calorimeter response is linear. The E/p for photons, π0, electrons in simulation are same as data within 1%.

  • The E/p of hadrons varies from ~0.55 to ~0.85.

Fraction of jet pt carried by particles with pt ptmax
Fraction of Jet Pt carried by particles with Pt<PtMax,

ΣPt (Pt<PtMax)/ΣPt(all)

Jet 20,50,70, 100

Tracks in cone R=0.7 around calorimeter jet axis

No tracking efficiency corrections

<JetPt>=24 Gev

55 GeV

Fraction of Jet Energy

Fraction of Jet Pt carried by particles

With Pt<400 MeV

95 GeV

150 GeV

320 GeV

250 GeV

Track Pt Max (GeV)

Pt Hadron-Jet

A large fraction of jet energy is carried by low Pt particles

Cal2had corrections
Cal2Had Corrections

JetClu R=0.4

Cal-Pt distributions for fixed Had-Pt Jets

  • Generate DiJet PYTHIA Tune A events from PtMin=0,10,…, 600 GeV.

  • Reconstruct jets from HEPG stable particles using CDF clustering algorithms

  • Reconstruct jets from Calorimeter towers using same algorithm

  • Select Had-Jet, find the closet calorimeter jet within ηφ spece, require R<0.1.

  • Calculate the correction for CalorJet Pt to recover Hadron-Pt.

8 GeV

20 GeV

Number of Events

200 GeV

50 GeV


Systematic uncertainty
Systematic Uncertainty

  • E/p measurement/modeling

  • P spectrum in jets (Tracking efficiency)

  • Time dependence of data calibration (assume detector simulation is fixed and agrees with data in limited time period.)

Calorimeter calibration
Calorimeter Calibration

Em Objects agree to 1%


Very good agreement up to 20 GeV.

For p> 20 relies on 1990 test beam. Test beam uncertainty ~2%. Not easy to check with data.

It affects higher Pt jets.

Measurement limited to central 36% of the tower. Need to check φ/η crack simulation further.

Sys Uncertainty

0 < p < 12 GeV 2%

12 < p < 20 GeV 3%

p> 20 GeV 4%




After background subtraction

Track momentum (GeV)

Uncertainty from e p calibration
Uncertainty from E/p calibration

  • Use GENP, stable particles within R=0.7 around hadron jet axis.

  • Calculate Cal Energy(E0)

    π0, photon, electron E/p=1.0

    Charged particles using Soon’s E/p curve on page 11.

  • Change E/p ↑↓ and calculate E+/E-

  • Plot E+/E0 and E-/E0


    0 < p < 12 GeV 2 %

    5 < p > 20 GeV 3 %

    p > 20 GeV 4 %

    (Run I-Run II understood to ~2%,

    1990 Test Beam 2%, CHA response)

    Photon/π0/electrons <1%, ignore.

    Run I

    p<15 GeV 5% difference between in situ calibration and test beam

    p>15 GeV 2% test beam calibration

Change in Jet Energy scale

PtMin 0,10,18,40,60,90,150,200,300,400,500

Fragmentation uncertainty
Fragmentation Uncertainty

  • If E/p was flat, uncertainty in Pt spectrum of particles in the jet will not lead to any uncertainty in energy scale.

  • Simon has compared P distribution in data and Pythia MC and show reasonable agreement except at low momentum particles for jets up to 100 GeV.

  • Need estimate the difference in Jet energy by keeping E/p fixed and changing P of particles.

  • HERWIG, wait and see. Photon Jet shows Pt spectrum is softer. How to assign systematic? Use HERWIG/Pythia difference as systematic?

Under reported energy for data and pythia tune a
Under-Reported Energy for Data and Pythia Tune A

Loss (ΣPt- ΣPt●Response(p))/PtCalJet

Compare energy loss in calorimeter between in data and MC

  • Use tracks for MC and Data.

  • Cone of R=0.7 around jet axis

  • (should be small effect as E/p is flat and large)

  • Not sensitive to Pt<0.3 GeV particles. (10% at 5 GeV, 1% Pt>50 GeV)

  • 5% change in tracking efficiency  2% changeeffective E/p

  • Check HERWIG sample

  • Need more checks but I think uncertainty is ~1-2%

Data Jet20,50,70,100

Pythia Tune A 00,…500

Pythia “loss” 1% lower for Pt<250 GeV

Uncertainty of out of cone corrections
Uncertainty of Out-of-Cone Corrections

Correcting back to parent parton

Compare energy just outside the cone in data/Pythia/MC

Photon-Jet data


W+Jet data HERWIG MC

0.5% uncertainty


Hard Δφ cuts/DiJet background not important/Quarks/gluons

New method: Compare fully corrected jet with photon

Jet Isolation Cut , mainly gluons

Dijet mass resolution
DiJet Mass Resolution

Reorganization of jet corrections group, di-jet mass, zb\bar b groups

  • Build on Di-Jet mass group work (Stefano, Andrea from Rockefeller)

  • Using tracking and ShowerMax/CPR information to correct the jet energies.

  • Tower Classification algorithm already in place.

  • A simpler algorithm based only on calorimeter and tracks (H1) is also working.

  • Working on improvements by correcting towers before clustering (Andrea Bocci wrote initial code).

  • Further studies to evaluate and optimize the algorithms.


  • Not discussed in this talk, the CDF calorimeter is properly calibrated and stable after run dependent calibrations. (Early in the run Plug calorimeter was found to be decaying very fast.)

  • We understand the origin of central calorimeter scale change from Run I to Run II.

  • The energy scale uncertainty is substantially reduce from Gen 4 analysis.

  • New uncertainty is almost same as Run I, if not slightly better.

  • Cal2Had Corrections

    E/p measurement/modeling <2% (10 GeV) to 2.6% (600 GeV)

    Fragmentation (need further studies/HERWIG) ~1-2%

  • Relative and Multiple p\bar p subtraction uncertainty has also decreased.

  • Out-of-Cone uncertainty 4.5—1%

  • Top mass papers soon. Corrections to finalized in next few weeks

  • Need some more work for very high Pt Jets

  • Refine/determine JetClu and MidPoint corrections (Kt clustering)

  • Further investigate Pythia/Herwig fragmentation.

  • There is still room for improvements specially in simulation, relative corrections at low Pt, Out-of-cone corrections, and understanding HERWIG/PYTHIA differences.