Jet Energy Corrections in CMS
Daniele del Re
Universita’ di Roma “La Sapienza” and INFN Roma
Outline
• Summary of effects to be corrected in jet reconstruction
• CMS proposal: factorization of corrections
• data driven corrections
– Strategy to extract each correction factor from data
• Perspectives for early data
– Priorities, expected precisions, statistics needed
Note: results and plots in the following are preliminary and not for
public use yet
02/19/07
Daniele del Re (La Sapienza & INFN)
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CMS Detector: Calorimetry
>75k lead tungstate crystals
PbWO4
30g/MeV
X0=0.89cm
crystal
lenght
~23cm
Front face
22x22mm2
HO
Had Barrel: HB
brass Absorber and
Had Endcaps: HE scintillating tiles+WLS
Had Forward: HF scintillator “catcher”.
Had Outer: HO iron and quartz fibers
HB
HE
HF
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Daniele del Re (La Sapienza & INFN)
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Jet reconstruction and calibration
• Calorimeter jets are reconstructed using
towers:
– Barrel: un-weighted sum of energy deposits in
one or more HCAL cells and 5x5 ECAL crystals
– Forward: more complex HCAL-ECAL
association
• In CMS we use 4 algorithms: iterative
cone, midpoint cone, SIScone and kT
– will give no details on algorithms, focusing on
corrections
• Role of calibration:
correct calorimeter jets back either to
particle or to parton jets (see picture)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Parton level vs particle level corrections
• In CMS
– Calojets are jets reconstructed from calorimeter energy deposits with a
given jet algorithm
– Genjets are jets reconstructed from MC particles with the same jet
algorithm
• Two options
– convert energy measured in jets back to partons (parton level)
– convert energy measured in jets back to particles present in jet
(particle level)
• Idea is to correct back to particle level (Genjets)
• Parton level corrections are extra and can be applied afterwards
02/19/07
Daniele del Re (La Sapienza & INFN)
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Causes of bias in jet reconstruction
• jet reconstruction algorithm
– Jet energy only partly reconstructed
• non-compensating calorimeter
– non-linear response of calorimeter
• detectors segmentation
• presence of material in front of calorimeters and magnetic
field
• electronic noise
• noise due to physics
– Pileup and UE
• flavor of original quark or gluon
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Daniele del Re (La Sapienza & INFN)
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Dependence of bias
• vs pT of jet
– Non-compensating calorimeter
– low pT tracks in jet
• vs segmentation
– large effect vs pseudorapidity h (large detector variations)
– small effect vs f (except for noisy or dead cal towers)
• vs electromagnetic energy fraction
– non-compensating calorimeter
• vs flavor
• vs machine and detector conditions
• vs physics process
– e.g. UE depends on hard interaction
02/19/07
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Dependence of bias vs causes
Original
quark/gluon
Physics noise
Electronic
noise
Material in
front of cal.
Segmentation
Noncompensating
Jet algorithm
vs pT
vs h
vs em fraction
vs flavor
vs conditions
vs process
Complicated grid: better to estimate dependences from data than
study each single effect
02/19/07
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Factorization of corrections
• correction decomposed into (semi)independent factors
applied in a fixed sequence
– choice also guided by experience from previous experiments
• many advantages in this approach:
–
–
–
–
each level is individually determined, understood and refined
factors can evolve independently on different timescales
systematic uncertainties determined independently
Prioritization facilitated: determine most important corrections
first (early data taking), leave minor effects for later
– better collaborative work
– prior work not lost (while monolithic corrections are either kept
or lost)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Levels of corrections
1.
2.
3.
4.
5.
6.
7.
Reco
Jet
Offset: removal of pile-up and residual electronic noise.
Relative (h): variations in jet response with h relative to control region.
Absolute (pT): correction to particle level versus jet pT in control region.
EM fraction: correct for energy deposit fraction in em calorimeter
Flavor: correction to particle level for different types of jet (b, t, etc.)
Underlying Event: luminosity independent spectator energy in jet
Parton: correction to parton level
L1
L2
L3
L4
L5
L1
L1
Offset
Rel:h
Abs:pT
EMF
Flavor
UE
Parton
Required
02/19/07
Calib
Jet
Optional
Daniele del Re (La Sapienza & INFN)
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Level 1: Offset
Goal: correct for two effects 1) electronic noise 2)
physics noise
1) noise in the calorimeter readouts
2a) multiple pp interactions (pile-up)
2b) (underlying events, see later)
•
additional complication: energy thresholds applied to reduce data
size
– selective readout (SR) in em calorimeter (ECAL)
– zero suppression (ZS) in had calorimeter (HCAL)
•
with SR-ZS, noise effect depends on energy deposit
– need to properly take into account SR-ZS effect before subtracting noise
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 1 Correction
1) take runs without SR-ZS triggered
with jets
– perform pedestal subtraction
– evaluate the effect of SR-ZS vs pT
Evaluate effect of red blobs
without ZS in data taking
no pileup
and noise
with pileup
and noise
Under threshold:
removed by ZS
Now over threshold:
not removed
 Apply ZS offline and calculate
multiplicative term:
no ZS
 ZS
corrZS ( E cut
/ E no
jet )  E jet
jet
2) take min-bias triggers without SR-ZS
– run jets algorithms and determine noise
contribution (constant term):
offset(h )
3) correct for SR-ZS and subtract noise
cut
cut
E cor
jet  E jet  corrZS ( E jet )  offset(h )
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 2: h dependence
Goal: flatten relative response vs h
• extract relative jet response with respect to barrel
– barrel has larger statistics
– better absolute scale
– small dep. vs h
Relative
Response
1
• extract
c(h , pT )  pTprobe(h ) / pTbarrel
•
Before
After
1
2
3
Jet h
4
h correction in bins of pT (fully
uncorrelated with the next
L3 correction)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 2: data driven with pT balance
• use of 2→2 di-jet process
• main selection based on
– back-to-back jets (x-y)
– events with 3 jets removed
Probe Jet “other jet”
y
Trigger Jet |η|<1.0
• di-jet balance with quantity
DijetPT  ( PT
B
PT
probe
 PT
 PT
DijetPT
probe
barrel
z
)/2
barrel
• response is extracted with
y
x
02/19/07
Probe Jet “other jet”
Daniele del Re (La Sapienza & INFN)
Trigger Jet |η|<1.0
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Level 2: Missing Projection Function
• MPF: pT balance of the full event
 tag  recoil 
pT  pT  E T  0
• in principle independent on jet algo
– purely instrumental effects
– less sensitive to radiation (physics modeling) in the event
... but depends on good understanding of missing ET
– need to understand whole calorimeter before it can be used
• Response ratio extracted as
Rrecoil
Rtag
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
tag
E T  nˆT
 1
tag
pT
Daniele del Re (La Sapienza & INFN)
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Level 3: pT dependence
Goal: flatten absolute response variation vs pT
• Balance on transverse plane (similar to L2 case), two methods:
– g + jet
 mainly qg->qy
 large cross section
 not very clean at low pT
– Z + jet
 relatively small cross
 cleanest
y
R( pT )  pTjet / pTg ,Z , probe
– rescale to parton level, extra MC correction needed from parton
to particle
x
• response is
• also MPF method (as for L2 case)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 3: g+jet example
• main bkg: QCD events (di-jet)
• selection based on
• ~1 fb-1 enough for decent
statistical error over pT range
pT(jet)/pT(g)
– g isolation from tracks, other em and had. deposits
– per event selection: reject events with multiple jets, g and jet
back-to-back in x-y plane
– but for low pT large contamination
from QCD (use of Z+jet there)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 4: electromagnetic energy fraction
Goal: correct response dependence vs relative energy
deposit in the two different calorimeters (em and had)
• detector response is different for em particles and hadrons
– electrons fully contained in em calorimeter
• fraction of energy deposited by hadrons in em calorimeter varies
and change response
• independent from other
corrections (h, pT)
• introducing em fraction correction
improves resolution
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 4: extract corrections
• start with MC corrections
• idea is to use large g+jet samples (not for early data)
• also possible with di-jet
• in principle used to improve resolution, no effect on bias.
Less crucial to have data driven methods.
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 5: flavor
Goal: correct jet pT for specific parton flavor
• L3 correction is for QCD mixture of quarks and gluons
• Other input objects have different jet corrections
– quarks differ from gluons
– jet shape and content depend on quark flavors
• heavy quark very
`different from light
– for instance b in 20% of
cases decays
semileptonically
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 5: data driven extraction
• correction is optional
– many analyses cannot identify jet flavors, or want special corrections
– correction desired for specialized analysis (top, h g bb, h g t t, etc.)
corrections from :
• tt events tt→Wb→qqb
– leptonic + hadronic W decay in event, tag 2b jets,
remaining are light quark
– constraints on t and W masses used
to get corrections
•
g+jets, using b tagging
• pp→bbZ, with Z→ll
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 6: UE
Goal: remove effect of underlying event
• UE event depends on details of hard scatter
 dedicated studies for each process
 in general this correction may be not theoretically
sound since UE is part of interaction
• plan (for large accumulated stats) is to use same approach
as L1 correction but only for events with one
reconstructed vertex
02/19/07
Daniele del Re (La Sapienza & INFN)
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Level 7: parton
Goal: correct jet back to originating parton
• MC based corrections: compare
Calojets after all previous corrections
with partons in bins of pT
– dependent on MC generators
(parton shower models, PDF, ...)
02/19/07
Daniele del Re (La Sapienza & INFN)
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Sanity checks
given
–
–
–
–
number of corrections
possible correlation between corrections
not infinite statistics in calculating corrections
smoothing in extracting corrections
sanity checks are needed
• after corrections, re-run g+jet balance and check that
distribution is flat
• cross-checks between methods should give same answer
– e.g. extract corrections from tt and check them on g+jet sample
02/19/07
Daniele del Re (La Sapienza & INFN)
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Plan for early data taking
• day 1: corrections from MC, including lessons from
cosmics runs and testbeams
• data<1fb-1: use of high cross-section data driven methods.
Tune MC
• longer term: run full list of corrections described so far
Integrated Minimum
luminosity time
Systematic
uncertaintiy
10 pb-1
>1 month
~10%
100 pb-1
>6 months
~7%
1 fb-1
>1 year
~5%
10 fb-1
>3 years
~3%
02/19/07
numbers do not take into account
1) low pT: low resolution, larger
backgrounds
 larger uncertainties
2) large pT: control samples have low
cross section
 larger stat. needed
Daniele del Re (La Sapienza & INFN)
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Conclusions
• CMS proposes a fixed sequence of factorized corrections
– experience from previous experiments guided this plan
• first three levels: noise-pileup, vs h and vs pT sub-corrections
represent minimum correction for most analyses
– priority in determining from data
• EM fraction correction improves resolution
• last three corrections: flavor, UE and parton are optional and
analyses dependent
• jet energy scale depends on understanding of detector
– very first data will be not enough to extract corrections (rely on MC)
– ~1fb-1 should allow to have ~5% stat+syst error on jet energy scale
02/19/07
Daniele del Re (La Sapienza & INFN)
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