1. Jet Energy and calibration with data at the CDF experiment
Monica D’Onofrio
3rd Top Workshop, from the Tevatron to ATLAS
Grenoble, 10/23/2008

2. Top Workshop@Grenoble, 10/23/2008
Outline
Introduction
CDF experiment and calorimeter
Jet Energy Scale correction: method
Calorimeter response
h-dependent corrections
Absolute correction
Multiple interactions
Underlying event and Out-of-cone Energy
Cross check of jet energy scale (photon/Z+jet data)
Other calibration signals: In-situ calibration for top
B specific jet corrections with Zbbbar
Note: MC samples used in JES  PYTHIA Tune A and HERWIG 6.5
Monica D'Onofrio (IFAE)
Top 10/23/2008

3. Top Workshop@Grenoble, 10/23/2008
Motivation
Knowledge of Jet Energy Scale (JES) is fundamental for hadron colliders
All physics processes involve jets that span a wide ET range [0,√s/2]
Important for SM measurements …
Phys Rev D75, (2007)
Inclusive jet cross section
Jet Energy Scale uncertainties
are dominant for high PT jets
A 1% uncertainty on the jet scale
translates to ~10% uncertainty for jet C.S. ~500 GeV PT and O(1%) on the top mass
Monica D'Onofrio (IFAE)
Top 10/23/2008

4. Top Workshop@Grenoble, 10/23/2008
Impact on top mass
Dilepton Top Mass channel:
 JES dominant systematic
Mainly dominated by Underlying
Event and Parton Shower uncertainties
Lepton+jets (DLM)
In lepton+jets and all hadronic channels, in-situ calibration is possible using the well-measured W boson mass:
JES systematics not dominant
still an important factor
Systematic source
Systematic uncertainty (GeV/c2)
Calibration
0.1
MC generator
0.5
ISR and FSR
0.3
Residual JES
b-JES
0.4
Lepton PT
0.2
Pileup
PDFs
Background
Total
1.0
Monica D'Onofrio (IFAE)
Top 10/23/2008

5. Tevatron and CDF@RunII
Highest-energy accelerator currently operational
CDF
Peak luminosity
 above 3.1 *1032 cm-2 s-1
Integrated luminosity/week
 about pb-1
CDF: ~4.2 fb-1 on tape
Silicon micro-vertex tracker
 Excellent tracking efficiency
Solenoid
High rate trigger/DAQ
L2 trigger on displaced vertices
Calorimeters and muons
Monica D'Onofrio (IFAE)
Top 10/23/2008

6. Top Workshop@Grenoble, 10/23/2008
Jet reconstruction
A jet is a composite object:
complex underlying physics
depends on jet definitions
Use different kind of Jet algorithms:
- Cone algorithms (JETCLU and MIDPOINT)
- KT algorithm
Corrections on Jet Energy Scale (JES) for
different effects:
Instrumental effects:
- response to hadrons
- poorly instrumented regions
- Multiple p-pbar interactions
Physics effects:
- Underlying event
- Hadronization
Time
Monica D'Onofrio (IFAE)
Top 10/23/2008

7. Top Workshop@Grenoble, 10/23/2008
CDF Calorimeter
Central and Wall ( |h|<1.2 ):
Granularity: Df × Dh = 15° × 0.1 (~ 800 towers)
Non compensating
 non-linear response to hadrons
Rather thin: 4 interaction lenghts
Small amount of noise
Resolutions:
EM energies (g,e): s/ET = 13.5%/√ET+1.5%
HAD energies(p±): s/ET = 50%/√ET+3%
Plug (1.2<|h|<3.6):
Similar technology to the central
Resolutions:
- EM energies (g,e): s/E = 16%/√E+1%
- HAD energies (p±): s/E = 80%/√E+5%
Thicker than central: 7 interaction lenghts
Monica D'Onofrio (IFAE)
Top 10/23/2008

8. Calorimeter calibration: EM energy
Check calorimeter response:
Use test beam (from 1980s!) and single particles measured in-situ to understand absolute response
Check time dependence
For EM energy response use:
MIP peak when possible
(at about 300 MeV)
Ze+e- mass peak stability
Set absolute EM scale
in central and plug
Monica D'Onofrio (IFAE)
Top 10/23/2008

9. Calorimeter calibration: Hadronic Energy
For hadron energy response use
Minimum Ionizing Particles (MIP):
J/ and W muons
Peak HAD calorimeter: ~ 2 GeV
Also Minimum bias events:
- E.g. N towers (ET>500 MeV)
Syst. Uncertainty related to
Calorimeter Calibration ~ 0.5%
Monica D'Onofrio (IFAE)
Top 10/23/2008

10. Calorimeter simulation
Use MC simulation to determine Jet Corrections
MC needs to
Simulate accurately detector response to single particle (E/p). CDF uses:
GEANT to track generated particles through the detector
Gflash for fast EM and HAD shower simulation, using parametrizations of longitudinal and lateral shower profiles
Describe jet fragmentation: MC tuned on data
Tuning based on in-situ
CDF data (dedicated triggers)
E/P response as a function
of particle momentum p.
Lateral profile shower
Monica D'Onofrio (IFAE)
Top 10/23/2008

11. Single particle response simulation
Test beam
In situ:
Select ‘isolated’ tracks
Measure energy in tower
behind them
Dedicated trigger
Bgk subtraction
Tune simulation to describe E/p distribution at each p
Monica D'Onofrio (IFAE)
Top 10/23/2008

12. Single particle response simulation
Jet composition:
~ 70 % charged particles
- 10% protons
- 90% pions
30 % neutral pions ( gg)
- EM response
hadrons
Remaining difference data/simulation  taken as syst. uncertainty
Monica D'Onofrio (IFAE)
Top 10/23/2008

13. Uncertainties on calorimeter simulation
Improved now with higher
statistical samples up to p~40 GeV/c
Sensitive to 0.9x0.9 = 81% inner part of the tower.
 For tower boundaries: additional 10% uncertainty
Total uncertainties:
Monica D'Onofrio (IFAE)
Top 10/23/2008

14. CDF Jet Energy Scale Method
Different correction factors:
(frel) Relative Corrections
 Make response uniform in h : all corrections are then referred to the central region
(MPI) Multiple Particle Interactions
 Energy from different ppbar interaction
(fabs) Absolute Corrections
 Calorimeter non-linear and non-compensating
PT jetparticle(R) = [ PT jetraw(R)  frel (R) – MPI(R)]  fabs(R)
Additional corrections to get to parton energy:
(UE) Underlying Event
Energy associated with spectator partons in a hard collision
Hadron-to-Parton correction (historically defined as Out-Of-Cone)
PT parton(R) = PT jet particle(R) - UE(R) + OOC
Systematic uncertainties are associated with each step
Monica D'Onofrio (IFAE)
Top 10/23/2008

15. Top Workshop@Grenoble, 10/23/2008
Relative Corrections
Jet Corrections are relative to the central calorimeter:
Central (0.2<|h|<0.6 jets)
~1 by definition (reference)
Difference Data/MC mainly in the
forward region
 Depends on ET jets considered
cracks
Monica D'Onofrio (IFAE)
Top 10/23/2008

16. h-dependent corrections
After corrections
Data
Pythia
PT-balancing also used to implicitly correct for transverse spreading of calorimeter showers outside jet cone + any h dependence of gluon radiation and multiple parton interactions
Systematic Uncertainties
Monica D'Onofrio (IFAE)
Top 10/23/2008

17. Multiple Interactions
Overlapping interactions
can overlap the jet
Number of extra interactions
depends on luminosity
LHC
Low lumi (L = 1× 1033 cm-2 s-1): <N>=2.3
High lumi (L = 1× 1034 cm-2 s-1): <N>=23
Tevatron
L = 2× 1032 cm-2 s-1: <N>=6
 Offset depending on number of interactions
Monica D'Onofrio (IFAE)
Top 10/23/2008

18. Multiple Interaction corrections
Linear correlation between number of interactions and number of vertices
Define random cones in the central region (0.2<|h|<0.6) and determine average
transverse energy associated to a cone
Cone-based method
For cone R = 0.7, <ET> = 1.06 GeV
Monica D'Onofrio (IFAE)
Top 10/23/2008

19. CDF Absolute Corrections
Use MC simulation  MC is adjusted by comparison with data to:
simulate accurately detector response to single particle (E/p).
Due to non-linearity of the calorimeter, non trivial correlation between N particles and PT track spectra
Very important a good understand of track efficiency
describe jet fragmentation
Measurement of jet shape is fundamental
Integrated jet shape
Data/MC difference  Systematic uncertainty ~ 1%
Monica D'Onofrio (IFAE)
Top 10/23/2008

20. Mapping the absolute scale
Map the calorimeter jet PT to the particle jet PT
Use di-jet events generated in PYTHIA (0-800 GeV).
Based on the most probable observed jet transverse momentum PTjet, given a particle jet with fixed value PTparticle
Probability density function dP(PTparticle,PTjet):
where DPT = Ptparticle – Ptjet and remaining parameters are used to model a double Gaussian function representing a core response and tails
Monica D'Onofrio (IFAE)
Top 10/23/2008

21. Jet corrections to particle level (absolute)
Unbinned likelihood fit used
to extract the response parameters
Almost independent on jet cone size.
Depends on transverse momentum: calorimeter response is ~ 70% for 25 GeV/c jets, ~ 90% for 400 GeV/c jets.
Absolute correction factor
After this correction, jets are independent of the detector.
Monica D'Onofrio (IFAE)
Top 10/23/2008

22. Top Workshop@Grenoble, 10/23/2008
Jet Fragmentation
Fraction of tracks VS P
If E/p was flat, uncertainty in Pt spectrum of particles in the jet will not lead to any uncertainty in energy scale.
Momentum distribution of charged tracks distribution in data and Pythia MC agree except at low momenta.
However, for same measured jet , total energy carried by charged tracks is different in data and Pythia (~a few %).
Pythia/Data scale differ by <1% for GeV jets. Take as systematic uncertainty.
HERWIG and PYTHIA agree to better than 1%.
Monica D'Onofrio (IFAE)
Top 10/23/2008

23. Absolute systematic uncertainties
CALORIMETER SIMULATION
(1.3  2.5%) uncertainty on
response of the calorimeter to
single particles (p, p, n, etc)
FRAGMENTATION(1%)takes into account
how well MC describe the particle spectra
and densities at all Jet ET
STABILITY (0.5%) Calorimeter
scale variation with time
Monica D'Onofrio (IFAE)
Top 10/23/2008

24. Model-dependent corrections
Underlying event (UE) and Hadron-to-Parton (Out-of-cone, OOC) energy corrections used only if need parton energy
Modeling are required, difference MCs as systematic uncertainties.
Method might be different depending on analysis (top mass reconstruction, Higgs boson searches)
Parton transverse momentum:
PT parton(R) = PT jet particle(R) - UE(R) + OOC
Monica D'Onofrio (IFAE)
Top 10/23/2008

25. Top Workshop@Grenoble, 10/23/2008
Underlying Event
Particle jet could have
contributions related to hard interaction:
Beam-beam renmants
Initial state radiation
MC tuned on Data (as Pythia Tune A)
Use di-jet events
Will be much harder at the LHC!!!
Monica D'Onofrio (IFAE)
Top 10/23/2008

26. Out-of-Cone Correction
OOC energy: energy escaping the cone radius
Gluon radiation (FSR)
Obtained from Pythia di-jet samples:
Ratio PTparton / PT jet particle
Similar performance Pythia and Herwig
Systematic uncertainties from photon+jet events:
Assume PTg = PT jet corr.
Difference Data/MC of energy inside annuli around jet axis taken as systematic uncertainty
 Take the largest difference
between Data/PYTHIA and
Data/HERWIG
 Up to 6% for low PT jets
Monica D'Onofrio (IFAE)
Top 10/23/2008

27. JES Systematic uncertainties
Total systematic uncertainties for JES
 between 2 and 3% as a function of corrected transverse jet momentum
High Pt: Dominated by calorimeter
simulation uncertainties
Low Pt: Dominated by
MC/data uncertainties
Monica D'Onofrio (IFAE)
Top 10/23/2008

28. Cross-checks using prompt photons
Photons are well measured in EM calorimeter
Complications:
number of events at high ET very low
Background due to p0
Purity % for [20-100] GeV photon transverse energy range
Use photon+jets (but also Z+jets) for cross check and to evaluate OOC corrections and JES systematic uncertainty due to Data/MC differences.
Monica D'Onofrio (IFAE)
Top 10/23/2008

29. Top Workshop@Grenoble, 10/23/2008
g (Z) + jet pT balance
pT photon > 27 GeV (trigger)
ET (second jet) < 3 GeV
Df (Jet-g) > 3
Sensitive to radiation effects
when allow second jet:
Herwig farther away from jet cone
Data
Pythia
Herwig
pT balance:
Agreement Data/MC within 3%
Monica D'Onofrio (IFAE)
Top 10/23/2008

30. Top Workshop@Grenoble, 10/23/2008
Z-jet pT balance
These events allow us to reach lower PT than photon+jet and also cross check photon+jets results.
Selection
two e(m) with ET>18 GeV (pT>20 GeV)
76 < M ee(mm) < 106 GeV
ET (second jet) < 3 GeV
Df (Jet-Z) > 3
Similar Herwig behaviour
for Z+jet w.r.t. g+jet but less visible
pT (Z)
Monica D'Onofrio (IFAE)
Top 10/23/2008

31. Comments on CDF procedure
CDF procedure demands a very accurate simulation of calorimeter showers and good understanding of underlying physics. It requires a detailed understanding of material in tracking volume, calorimeter response to single particles as well as particle Pt spectrum in jets (good knowledge of track reconstruction efficiency in high multiplicity environment). A lot of work has been done!
Ensuring good simulation implies that simulated data can be directly compared with real data in the variable of one’s choice (e.g. size of τ Jet).
Easy to build upon to improve jet resolution.
Same procedure valid for all Pt Jets, even at 500 GeV jets.
The procedure depends only on jet/calorimeter simulation and is independent of extra radiation in the event.
Various stages of corrections allow users to do analysis at calorimeter-level, particle-level, particle-level after UE subtraction, parton-level, depending on physics.
Monica D'Onofrio (IFAE)
Top 10/23/2008

32. Contraining JES with Dijet mass resonances & b-jet specific corrections
- W from top decays
- Z in bb decay mode

33. Calibration Peaks from W’s
In situ calibrations in ttbar samples:
 take the single dijet mass closest to the well known W mass as the single value of mjj per event
Monica D'Onofrio (IFAE)
Top 10/23/2008

34. JES on top mass with in-situ Wjj
One example (lepton+jets Multivariate method)
mt = ± 1.0 (stat.) ± 0.9 (JES) GeV/c2
Measured value for ΔJES, which is:
ΔJES = 0.09 ± 0.29 σ
BUT: “One point” calibration  cannot constrain JES over wide PT range.
(residual dependence on jet PT and h)
Not specific for b-jet
(b-specific uncertainty)
Monica D'Onofrio (IFAE)
Top 10/23/2008

35. Top Workshop@Grenoble, 10/23/2008
B-jet energy scale
B-jet response is expected to be different from light quarks or gluon jets responses:
Harder fragmentation
B-hadron decays (semileptonic fraction)
Dependence on tagging procedure used to identify b-jets
Use MC to model b-jet response
Apply generic jet energy corrections
Additional corrections (from MC) to correct b-jets back to the parent b-quark
Imply additional uncertainties (~ %)
based on constraints from other experiments
Test in data using g+bjet or Zbbbar
Monica D'Onofrio (IFAE)
Top 10/23/2008

36. Top Workshop@Grenoble, 10/23/2008
Z  bbbar signal
Published on NIM
doi: /j.nima  
b-jet energy scale from Z signal
tools to extract DiJet mass resonances (Hbb)
Trigger on two displaced tracks+ two 10 GeV jets
DisplacedVertex tag (2-tags required) , SecondaryVertex Mass to select b-jets
kinematic cuts to improve S/B
Fit signal and background (direct QCD production) templates, for varying JES
1 tag
extract a signal of
N = 5674 ± 448(stat.)
Z → bbbar decays in low-radiation central dijet events
Result of a constrained unbinned likelihood fit performed to double tagged dijets data (blue points). The signal is constrained to the number of expected events (gaussian constraint: 4630 ± 727 Gaussian). The data-driven background shape and Monte-Carlo signal p.d.f are shown respectively in green and red. The fit returns 5674 ± 448 events of signal and a b-JES of ± (errors are statistical only). The inset on the upper right shows the data minus background distribution (blue points) and the signal shape (in red) normalized to the fitted number of events of signal.
2 tag
Monica D'Onofrio (IFAE)
Top 10/23/2008

37. Top Workshop@Grenoble, 10/23/2008
b-jet energy scale from Z  bb
Constrained unbinned likelihood fit performed to double tagged dijets data.
Signal constrained to Nexp. events (gaussian constraint: 4630 ± 727 Gaussian).
DiJet Invariant mass
σZ x BR(Z->bb) =
1578 ± 123(stat) (syst) pb = (stat+syst) pb.
Theory (NLO): σZxBR(Z->bb) = 1129 ± 22 pb
B-Jet energy scale:
0.974 ± ( stat.) ± (sys.)
(agreement with 1 sigma of nominal scale factor)
Result of a constrained unbinned likelihood fit performed to double tagged dijets data (blue points). The signal is constrained to the number of expected events (gaussian constraint: 4630 ± 727 Gaussian). The data-driven background shape and Monte-Carlo signal p.d.f are shown respectively in green and red. The fit returns 5674 ± 448 events of signal and a b-JES of ± (errors are statistical only). The inset on the upper right shows the data minus background distribution (blue points) and the signal shape (in red) normalized to the fitted number of events of signal.
Monica D'Onofrio (IFAE)
Top 10/23/2008

38. Summary and Conclusions
CDF Jet Energy Scale done in several steps
Tunes simulation and derives (part of the) corrections from MC
Many ‘calibration’ signals:
MIP peak, Zee and Minimum Bias for calorimeter
Di-jet balance for relative response in cracks and plug
Isolated tracks for calorimeter response
Photon/Z-jet balance for cross-check and systematic uncertainties
2-3% systematic uncertainty achieved depending on jet transverse energy
Can be reduced in specific measurement with in-situ calibrations (Wjj in lepton+jets top samples)
Approach for b-jet correction still rely on MC
Use of Z bbbar very promising
For more details on JES at CDF
arXiv:hep-ex/ , published on NIM /j.nima
Monica D'Onofrio (IFAE)
Top 10/23/2008

39. Back-up

40. Top Workshop@Grenoble, 10/23/2008
Jet Algorithms
Monica D'Onofrio (IFAE)
Top 10/23/2008

41. Clusters using different Jet algorithms
Monica D'Onofrio (IFAE)
Top 10/23/2008

42. Top Workshop@Grenoble, 10/23/2008
Details on GFlash (1)
Simulation of EM and Hadronic showers involves two steps
GFLASH calculated spatial distribution of energy, Edp, deposited by a shower w/in the calorimeter volume:
depends on incident particle energy, shower fluctuations, sampling structure of detector
Fraction of deposited energy visible to active medium, is calculated. Depends on
relative sampling fractions of MIPs, EM and Hadronic particles: Sem/Smip , Shad/Smip (tunable
parameters)
Longitudinal shower profiles modeled with Gamma-distribution. Hadronic showers classified 3-ways:
● Purely hadronic (h), scales w/absorption length, l0
● Showers w/ p0 produced in 1st inelastic collision (f)
● Showers w/ p0 produced in later inelastic collision (l)
22 parameters total
Monica D'Onofrio (IFAE)
Top 10/23/2008

43. Top Workshop@Grenoble, 10/23/2008
Details on GFlash (2)
Transverse/Lateral shower profiles for both EM and HAD particles are modeled with the Ansatz function:
R50 is given in units of Moliére Radius (RM) / Absorption Length (l0) for EM/HAD showers respectively.
The lateral spreading is taken to be linear (n=1) in HAD showers and quadratic (n=2) in EM showers.  14 parameters total
Possible 38 parameters
11 are tuned for central calorimeter
7 are tuned for the plug (forward) calorimeter
Remaining parameters use default settings from H1 Collaboration
Relies on relative independence of shower profiles to particular calorimeter
Tuning in-situ w/ isolated track data: E/p measurements w/ isolated charged particles
Monica D'Onofrio (IFAE)
Top 10/23/2008

44. Top Workshop@Grenoble, 10/23/2008
Lateral profile
Measure E/p signal in 5 towers adjacent in h
signal defined as 1×3 strip in φ
Plot E/p vs. relative eta for 5 towers
In Gflash, use formula for lateral profile
EM and HAD calorimeter probe different parts of the hadronic shower
excluding
90° crack
E/p vs ηrel
(Central)
Monica D'Onofrio (IFAE)
Top 10/23/2008

45. Calorimeter simulation
Use MinBias or isolated track trigger
Select good tracks within central 81% of tower.
No extra track within 7x7 towers, no ShowerMax cluster.
Measure E/p in data
Tune Gflash parameters
Difference in data and simulation is taken as uncertainty.
E(HAD)/p
E(EM)/p
E(Total)/p
After BG subtraction
More statistics!
Monica D'Onofrio (IFAE)
Top 10/23/2008

46. Top Workshop@Grenoble, 10/23/2008
Corrections for FLAT input spectrum.
Additional correction needed to unsmear
(almost negligible, high granularity in dpT )
Flat vs. QCD Spectra
Avg
For both spectra
There is an average PT shift of hadron jets to calorimeter jets.
With a Flat spectrum.
After accounting for the average shift there are roughly as many low PT as high PT jets “smearing” into the calorimeter PT bin.
With a QCD spectrum
After accounting for the average shift, there are significantly more low PT jets than high PT jets “smearing” into the calorimeter PT bins.
The QCD spectrum correction is therefore significantly lower.
Hadron Jet PT
Calorimeter Jet PT
Avg
Hadron Jet PT
Calorimeter Jet PT
Monica D'Onofrio (IFAE)
Top 10/23/2008

47. Top Workshop@Grenoble, 10/23/2008
Photon+jet balancing
Herwig Pythia Data
Δφ> 3, No 2nd jet cut
PT balance between photon and jet is about 3% different among data and MC.
Δφ>3 , second Jet Pt<3 GeV
Herwig Pythia Data
Monica D'Onofrio (IFAE)
Top 10/23/2008

48. Top Workshop@Grenoble, 10/23/2008
Uncertainties on OOC
The transverse energy around a jet of cone size Rjet is measured by adding the transverse energy in towers within the annulus defined by radii r1 and r2 around the jet axis
Systematic uncertainty of OOC corrections for different cone sizes. The systematic
uncertainty is taken as the largest difference between data and either PYTHIA or HERWIG
Monica D'Onofrio (IFAE)
Top 10/23/2008

49. Jet Resolution (H1 Algorithm)
Apply relative corrections to make response flat in η.
Use tracks (0.5<Pt<15 GeV, Pt ordered), extrapolate to face of calorimeter
Select towers within Δη=0.1 and Δφ=0.2. (Central towers are 0.1x0.26.) Take the nearest tower one if none within these limits.
Order selected towers in distance from the track.
Remove towers such that corresponding removed energy is always less or equal to the energy of the track. Energy already removed by a previous track is not considered by subsequent tracks.
Jet is sum of all quality-selected
tracks and remaining towers in the jet.
Scale the final jet energy
There is improvement (10-15%)
Monica D'Onofrio (IFAE)
Top 10/23/2008

50. Top Workshop@Grenoble, 10/23/2008
D0 Calorimeter
LAr sampling
U absorber: Compensating
 linear response to hadrons
7 interaction lengths
Same structure for barrel and plug
Resolutions:
EM energies (g,e): s/ET = 15%/√ET+0.3%
HAD energies(p±): s/ET = 45%/√ET+5%
Monica D'Onofrio (IFAE)
Top 10/23/2008

51. D0 Jet Energy Scale Method
Different correction factors:
(fabs) Absolute Corrections
 Calorimeter non-linear and non-compensating
(frel) Relative Corrections
 Make response uniform in h : all corrections are then referred to the central region
(O) Offset correction
 For MPI, underlying event and detector noise
(S) Showering correction
 For detector effect of energy leaking inside or outside of jet cone
ET jetparticle = [ ET jetraw -O] / (frel fabsl S)
Note D0 correct to a particle level with corrections for underlying event, but not
for out of cone corrections (different from CDF).
Monica D'Onofrio (IFAE)
Top 10/23/2008

52. Top Workshop@Grenoble, 10/23/2008
D0 response function
Monica D'Onofrio (IFAE)
Top 10/23/2008

53. Top Workshop@Grenoble, 10/23/2008
D0 Offset Energy
Corrects for all energy not associated to the hard scatter: MPI, underlying event and electronic noise
Worked out from minimum bias events
Monica D'Onofrio (IFAE)
Top 10/23/2008

54. D0 Relative and Absolute Corrections
Performed with photon-jet events
Similar corrections for different η
→shows relative corrections ok
Use dijet and photon-jet events.
Jet corrections relative to the central calorimeter |h|<0.6 :
 Depends on ET jets considered due to crack
Monica D'Onofrio (IFAE)
Top 10/23/2008

55. D0 Showering Correction
Use MC to estimate energy smeared in or out due to detector effects (this is absorbed in the absolute corrections at CDF)
Checks with data to evaluate the systematic error
Does not account for true energy from the parton distributed outside the jet radius (OOC corrections at CDF)
Monica D'Onofrio (IFAE)
Top 10/23/2008

56. JES Systematic uncertainties
Total systematic uncertainties for JES
between 2 and 3% as a function of corrected transverse jet momentum
CDF
Similar between CDF and D0 apart from out of cone correction, which
is very large at low Pt for CDF
Monica D'Onofrio (IFAE)
Top 10/23/2008