# Presentation: 144692

## Jet Energy Calibration

- Beate Heinemann
- University of Liverpool

- Fermilab, August 14th 2006

## Outline

- Introduction
- CDF and D0 calorimeters
- Response corrections
- Multiple interactions
- -dependent corrections
- Underlying event and Out-of-cone energy
- Other calibration signals
- Conclusions

- Disclaimer:
- Most discussion here valid for cone jets
- Will make some comments on kT jets
- Will discuss CDF and D0 procedures as examples
- ATLAS and CMS have no settled yet

## Partons are produced in hard scatter

- Would like to know the energy of these partons

## The parton will hadronise

- Hadronization is non-perturbative QCD phenomenon:
- Phenomenological models implemented in MC:
- Lund-Strong Model: PYTHIA
- Cluster fragmenation: HERWIG

- Depends on energy and quark type

## Multiple pp Interactions

- Overlapping interactions can overlap the jet
- Number of extra interactions depends on luminosity
- LHC:
- Low lumi (L=1x1033 cm-2s-1):   <N>=2.3
- High lumi (L= 1x1034 cm-2s-1): <N>=23
- Tevatron:
- L= 2x1032 cm-2s-1: <N>=6

- Offset depending on number of interactions

## More than one parton per proton interacts

- Spectator partons can interact also and put energy into the same area as hard interaction

- Offset, can depend on physics process

## Hadrons enter calorimeter

- Calorimeter response determines what we measure

- Correction depends on jet energy

## Calorimeter response depends on angle

- Often calorimeters are different in forward vs central region
- There are often poorly instrumented regions (cracks) that have lower response

- Correction depends on jet angle and energy

## Noise can overlap with jet 

- Depending on noise level in calorimeter the noise overlapping with our jet can be significant

- Offset depending on calorimeter noise level

## CDF calorimeter

- Central  and Wall (||<1.2):
- Scintillating tile with lead (iron) as absorber material in EM (HAD) section
- Coarse granularity: ~800 towers
- Non-compensating
- non-linear response to hadrons
- Rather thin: 4 interaction lengths
- Nearly no noise
- Resolutions:
- EM energies:    /E=13.5% / √E  1.5%
- HAD energies:  /E=50% / √E  3%
- Plug (1.2<||<3.6):
- Similar technology to central
- Resolution:
- EM energies:   /E=16 % / √E  1%
- HAD energies: /E=80 % / √E  5%
- Thicker: 7 interaction lengths

- Wall Had

- Central

- Plug

## DØ Calorimeter

- Same technology in central and forward calorimeter:
- Liquid Argon with iron (steal) as absorber in EM (HAD) calorimeter
- Fine granularity: ~50K cells
- Depth:
- 7.2-8.0 interaction lengths
- Compensating:
- Compromised in Run 2:
- Integrate charge only in 260ns due to shorter bunch spacing
- Resolutions:
- EM energies:  /E=15% / √E  0.3%
- HAD energies:/E=50% / √E  4%

- Online calibration: see N. Hadley’s lecture

## In Situ Calorimeter Calibration: Hadronic Energy 

- Minimum Ionising Particle (MIP):
- J/ and W muons
- peak in HAD calo: ≈2 GeV (in CDF)
- Check time stability
- Minimum bias events
- E.g. Ntower(ET>500 MeV)

- CDF

## In Situ Calorimeter Calibration: EM Energy 

- MIP peak:
- If visible (CDF at 300 MeV)
- Z ee peak:
- Set absolute EM scale in central and plug
- E/p for electrons
- After having calibrated p and material (see M. Shapiro’s lecture)
- Minimum Bias events:
- Occupancy above some threshold: e.g. 500 MeV

- CDF

## Calibrating jets at a Hadron Collider

- Hadron collider:
- Physics processes span entire jet ET range: 0<ET<√s/2
- Calibration processes (photon-jet) run out of steam much earlier:
- E.g. d()/dpT = 0.001 d(jet)/dpT
- Unlike at HERA (NC process) or LEP/SLC (Z-resonance)

## Two different approaches

- CDF and DØ use very different approaches
- Documented in
- CDF Run 2: hep-ex/0510047 (accepted by NIM)
- DØ Run 1: NIM A424: 352-394 (1999)
- DØ Run 2: http://www-d0.fnal.gov/phys_id/jes/public/plots_v7.1/index.html
- Main difference:
- CDF uses test beam and single particles measured in-situ to understand absolute response of single particles
- deduce jet response using simulation
- Cross check with calibration processes like photon-jet data
- DØ uses photon-jet data to measure absolute response
- Extra correction for “showering” necessary
- Other differences:
- CDF corrects separately for underlying event, multiple interactions, out-of-cone energy
- DØ includes all these effects into one correction factor

## Overview: CDF and DØ

- Ecorr: calibrated jet E
- Eraw: raw jet E
- F: eta-dependent correction
- R: absolute response
- O: offset energy
- includes MI, noise, UE
- S: showering corrections

-  Systematic error associated with each step
-  additional corrections to get to parton energy

- CDF calibrates PT
- PTcorr: calibrated jet PT
- PTraw: raw jet PT
- F: eta-dependent correction
- R: absolute response
- MI: multiple interactions

## CDF: Detector to Particle Level

- Do not use data since no high statistics calibration processes at high ET>100 GeV
- Extracted from MC  MC needs to
- Simulate accurately the response of detector to single particles (charged pions, photons, protons, neutrons, etc.): 	 				CALORIMETER SIMULATION
- (CDF uses fast parameterization GFLASH, D0 uses GEANT3)
- Describe particle spectra and densities at all jet Et: 	      	                                                           FRAGMENTATION
- Measure fragmentation and single particle response in data and tune MC to describe it
- Use MC to determine correction function to go from observed to “true”/most likely Et:
- 		Etrue=f ( Eobs, , conesize)

## Single Particle Response Simulation

- Single particle response:
- Test beam
- In situ:
- Select “isolated” tracks and measure energy in tower behind them
- Dedicated trigger
- Perform average BG subtraction
- Tune simulation to describe E/p distributions at each p (use π/p/K average mixture in MC)

- CDF

## Single Particle Response Simulation

- MC models
- Hadron response at low pT (in situ data) and high pT (test beam data)
- Electron response

- CDF

- electrons

- Typical jet composition:
- 60% charged particles
- 10% protons
- 90% pions
- 30% neutral pions ()
- EM response
- 10% other (neutrons,...)

## Fragmentation

- Due to non-linearity of calorimeters big difference between e.g.
- one 10 GeV pion: ~8 GeV
- ten 1 GeV pions: ~ 6 GeV
- Measure PT spectra of particles in jets at different ET values as function of track PT:
- Typically mean rather low
- Requires understanding track efficiency inside jets

- <ptrackT>=2.8 GeV

- <ptrackT>=5.1 GeV

- CDF

## Jet Correction to Particle Level

- MC convolutes response and particle momentum spectrum for us
- Use tuned and validated MC to compare measured jet to jet at particle level
- systematic uncertainty given by how well MC simulation and fragmentation reproduced data

- CDF

## CDF: Absolute Calorimeter Response

- Nearly independent of cone size
- Response about 80% at pT=50 GeV, 87% at pT=300 GeV

- CDF

- 1/Jet Response

## Response correction using prompt photons

- Prompt photon process:
- Photon well measured in calorimeter
- Calibrated using electrons
- Constraint: ET()=ET(jet)
- Complications:
- Number of events at high ET rather low:
- ET()>300 GeV, ∫Ldt=1 fb-1: 40 events
- Background due to 0’s
- Purity: 30-80% for ET()=20-100 GeV
- Higher order processes:
- Photon + 2 jets

## DØ using prompt photons

- Reduce “physics effects”:
- “MPF method”:
- MPF=Missing Et Projection Fraction
- Require jet to be back-to-back with photon:
- >3 radians (>172o)
- Reach high ET,jet:
- Calibrate versus energy Ejet
- Exploiting similarity between forward and central calorimeters
- jet ≈ 0: Ejet ≈ ET,jet
- jet ≈ 2: Ejet ≈ 3 ET,jet

## Syst. Uncertainties on Response

- Varying assumptions gives systematic uncertainty
- In analysis data/MC difference counts in most cases
- Same procedure done for MC

-  purity and scale syst.

-  energy scale

- 0 energy scale

## Multiple Interactions (MI)

- Need to know how many interactions there were:
- # of z-vertices ~ # of interactions
- Throw random cones in Minimum Bias events
- Determine average ET per cone, e.g. CDF: 1 GeV for R=0.7

- LHC

- TeV

- 40%

- 20%

## The complication for kT algorithm

- Multiple Interactions are main reason for the difficulties with the kT algorithm at hadron colliders
- The method of throwing a random cone does not work:
- they are not cone jets
- kT algorithm biases itself to go where the energy is and picks up energy from MI
- kT algorithm has now been used by CDF in Run 2 for the jet cross section:
- Empirical correction factor using fact that cross section independent of inst. luminosity

- Before correction

- After correction

- (L>50)/ (L<30)

- (L>50)/ (L<30)

## Relative Corrections

**Trigger jet or photon**

## Relative Corrections

- Mapping out cracks and response of calorimeter
- Central at ~1 by definition
- D0:
- Response similar in central and forward
- Two rather large cracks
- CDF:
- Response of forward better than of central
- Three smaller cracks
- Difficulties:
- depends on ET
- Can be (most often is initially) different for data and MC

- Cracks

## Corrections from Particle Jet to Parton

- Underlying event (UE) and Out-of-cone (OOC) energy
- Only used if parton energy is wanted
- Requires MC modeling of UE and OOC
- Differences are taken as systematic uncertainty

## Underlying Event

- Underlying event definition:
- “beam-beam remnants”: energy from interaction of spectator partons
- “Initial state radiation”: energy radiated off hard process before main interaction
- Not wanted when e.g. measuring the top quark mass
- Can be estimated using Monte Carlo
- Measurements led to tuning of MC generators: PYTHIA, Herwig+Jimmy

## Measuring the Underlying Event

- Many studies exist about underlying event:
- Checkout talks by Rick Field/U. of Florida
- At LHC we will need to measure it:
- Expect it to be much harder than at Tevatron

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

- Leading Jet Direction

## Out of Cone Energy (OOC)

- Out-of-Cone Energy:
- Original parton energy that escapes the cone
- E.g. due to gluon radiation
- Jet shape in MC must describe data:
- measure energy flow in annuli around jet
- Differences between data and MC
- Lead to rather large systematic uncertainty

- Data

- Pythia

- Herwig

- 40

- 80

- 120   PT (GeV)

- 0.04

- 0.06

- 0.06

- 0.04

- 0.02

- 0.02

- 0.0

- (OOCData-OOCMC)/pT

- Herwig

- Pythia

## Jet Energy Scale Uncertainties

- CDF and DØ achieve similar uncertainties after following very different paths before
- Both collaborations have plans to improve further

## Compare data and MC after calibration

- Data and MC agree within systematic uncertainties

## Photon-Jet PT balance

- Photon-Jet PT balance

- Agreement within 3% but differences in distributions
- Data, Pythia and Herwig all a little different
- These are physics effects!

- pTjet/pT-1

- CDF

## Z-jet PT balance

- Better agreement of data and MC than in photon-jet data
- In progress of understanding this better together with Herwig and Pythia authors

- pTjet/pTZ-1

- pTjet/pTZ-1

- CDF

## Calibration Peaks from W’s and Z’s

- Very, very difficult to see inclusive decays of W’s and Z’s to jets
- Small signal on huge background
- W+2 jets
- Photon+2 jets (UA2)
- Two best opportunities:
- W in top quark decays
- Z in bb decay mode

- UA2, S/B ~ 1/35, ~5000 Signal

## Additionally, use Wjj mass resonance (Mjj) to measure the jet energy scale (JES)  uncertainty

- Additionally, use Wjj mass resonance (Mjj) to measure the jet energy scale (JES)  uncertainty

*In-situ* Measurement of JES

- Mjj

- Measurement of JES scales directly with data statistics

- 2D fit of the invariant mass of the non-b-jets
- and the top mass:
- JES M(jj)- 80.4 GeV/c2

## Fit for ratio of JES in data to JES in MC

Constrain JES to 2% using 166 events

- Constrain JES to 2% using 166 events

**CDF (1 fb****-1****):    ********JES ****= 0.99 ± 0.02**
**DØ (0.3 fb****-1****):   ********JES ****= 0.99 ± 0.03**

- Wjj Calibration in Top Events

- (jet)

- At LHC will have 45,000 top events/month!

## Z->bb

- Zbb decay mode:
- Suppresses QCD background more than signal
- Difficult to trigger
- CDF uses secondary vertex trigger
- D0 uses semi-leptonic decays collected by muon trigger
- Use this to measure difference between data and MC JES, e.g. DØ:
- Data:
- =81.0 +/- 2.2
- =10.7 +/- 2.1
- MC:
- =83.3
- =13.0

## Conclusions

- Different calorimeters/collaborations can choose very different procedures:
- CDF tunes simulation and then derives everything from MC
- Systematic uncertainties depend on how well MC models data
- DØ does a purely data based estimate
- Systematic uncertainties depend on understanding of calibration process and sample composition
- Calibration signals:
- MIP peak, E/p, Zee and Minimum Bias for calorimeter calibration
- Di-jet balancing for relative response in cracks and in plug calorimeter
- Isolated tracks for understanding calorimeter response to π’s
- fragmentation needs to be modeled well
- Photon-jet balancing for relative and absolute response
- Independent channels used for cross checks/systematic error:
- Photon-Jet and Z-jet balancing
- Zbb peak and Wjj peak in top events
- 3-4% systematic uncertainty achieved so far
- Better for jets in top events (~2%)

- Jets are very complex and rather tough to calibrate

## Backup 

## Jet Energy Scale

- Jet energy scale
- Determine the energy of the partons produced in the hard scattering process
- Instrumental effects:
- Non-linearity of calorimeter
- Response to hadrons
- Poorly instrumented regions
- Physics effects:
- Initial and final state radiation
- Underlying event
- Hadronization
- Flavor of parton
- Test each in data and MC

## Offset correction in D0

- Offset includes:
- Underlying event
- Multiple interactions:
- # of Interactions ~ # of z-vertices
- Noise
- Pile-up from previous interaction
- Due to long shaping time of preamplifier
- Measure
- Minimum bias events per tower
- Depending on number of vertices