jets in atlas
DESCRIPTION
Jets In ATLAS. Peter Loch University of Arizona Tucson, Arizona USA. Preliminaries. This talk Focus on explanation of algorithms, methods, and measureable jet features Including some pointers to underlying principles, motivations, and expectations Some reference to physics - PowerPoint PPT PresentationTRANSCRIPT
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Jets In ATLASJets In ATLAS
Peter LochPeter Loch
University of ArizonaUniversity of Arizona
Tucson, ArizonaTucson, Arizona
USAUSA
Peter LochPeter Loch
University of ArizonaUniversity of Arizona
Tucson, ArizonaTucson, Arizona
USAUSA
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2P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Preliminaries
This talkThis talk Focus on explanation of algorithms, methods, and Focus on explanation of algorithms, methods, and
measureable jet featuresmeasureable jet features Including some pointers to underlying principles, motivations, Including some pointers to underlying principles, motivations,
and expectationsand expectations Some reference to physicsSome reference to physics
Restricted to published or blessed materialRestricted to published or blessed material Most performance expectations from “The ATLAS Experiment Most performance expectations from “The ATLAS Experiment
at the Large Hadron Collider” (G.Aad et al., 2008 JINST 3 at the Large Hadron Collider” (G.Aad et al., 2008 JINST 3 S08003)S08003)
Most results shown >1 year oldMost results shown >1 year old Everything is based on simulationsEverything is based on simulations
Experiments may tell a (very?) different story in some casesExperiments may tell a (very?) different story in some cases LHC beam conditions used for most studies are not LHC beam conditions used for most studies are not
appropriate for first dataappropriate for first data Center of mass 14 TeVCenter of mass 14 TeV
Mostly a phase space limitation in the first data for basic jet performance studies
No pile-up effects included except where especially statedNo pile-up effects included except where especially stated
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3P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Roadmap
1.1. Jets at LHCJets at LHCa.a. Where do they come from?Where do they come from?
Selected hadronic final statesSelected hadronic final states
b.b. Physics collision environmentPhysics collision environment Underlying event and pile-upUnderlying event and pile-up
2.2. Hadronic signals in ATLASHadronic signals in ATLASa.a. The ATLAS detectorThe ATLAS detector
Focus on calorimetersFocus on calorimeters General response issuesGeneral response issues
b.b. Signals for jet reconstructionSignals for jet reconstruction Unbiased and biased towersUnbiased and biased towers Topological clustersTopological clusters
3.3. Jet measurementJet measurementa.a. Jet input objectsJet input objects
TowersTowers Topological clustersTopological clusters Particles Particles
b.b. Jet reconstruction and calibrationJet reconstruction and calibration Reconstruction sequenceReconstruction sequence Calibration schemesCalibration schemes
4.4. Jet reconstruction performanceJet reconstruction performancea.a. QCD di-jetsQCD di-jetsb.b. Photon/z + jet(s)Photon/z + jet(s)c.c. W mass spectroscopyW mass spectroscopyd.d. Jet vertices and track jetsJet vertices and track jets
5.5. Conclusions & OutlookConclusions & Outlook
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4P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
1. Jets at LHC 1. Jets at LHC
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5P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Where do Jets come from at LHC?
t bW jjj
t bW l jj
t bW jjj
t bW l jj
1.8 TeVs
14 TeVs
(TeV)Tp
inclusive jet cross-section
qq q q WW Hjj qq q q WW Hjj
2
0
nb
TeVT
d
d dp
Fragmentation of gluons and Fragmentation of gluons and (light) quarks in QCD (light) quarks in QCD scatteringscattering Most often observed interaction at Most often observed interaction at
LHCLHC
Decay of heavy Standard Decay of heavy Standard Model (SM) particles Model (SM) particles Prominent example:Prominent example:
Associated with particle Associated with particle production in Vector Boson production in Vector Boson Fusion (VBF)Fusion (VBF) E.g., HiggsE.g., Higgs
Decay of Beyond Standard Decay of Beyond Standard Model (BSM) particlesModel (BSM) particles E.g., SUSYE.g., SUSY
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6P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
t bW jjj
t bW l jj
t bW jjj
t bW l jj
qq q q WW Hjj qq q q WW Hjj
top mass reconstruction
Fragmentation of gluons and Fragmentation of gluons and (light) quarks in QCD (light) quarks in QCD scatteringscattering Most often observed interaction at Most often observed interaction at
LHCLHC
Decay of heavy Standard Decay of heavy Standard Model (SM) particles Model (SM) particles Prominent example:Prominent example:
Associated with particle Associated with particle production in Vector Boson production in Vector Boson Fusion (VBF)Fusion (VBF) E.g., HiggsE.g., Higgs
Decay of Beyond Standard Decay of Beyond Standard Model (BSM) particlesModel (BSM) particles E.g., SUSYE.g., SUSY
Where do Jets come from at LHC?C
ER
N-O
PEN
-20
08
-02
0
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7P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Fragmentation of gluons and Fragmentation of gluons and (light) quarks in QCD (light) quarks in QCD scatteringscattering Most often observed interaction at Most often observed interaction at
LHCLHC
Decay of heavy Standard Decay of heavy Standard Model (SM) particles Model (SM) particles Prominent example:Prominent example:
Associated with particle Associated with particle production in Vector Boson production in Vector Boson Fusion (VBF)Fusion (VBF) E.g., HiggsE.g., Higgs
Decay of Beyond Standard Decay of Beyond Standard Model (BSM) particlesModel (BSM) particles E.g., SUSYE.g., SUSY
t bW jjj
t bW l jj
t bW jjj
t bW l jj
qq q q WW Hjj qq q q WW Hjj
Where do Jets come from at LHC?C
ER
N-O
PEN
-20
08
-02
0
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8P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Fragmentation of gluons and Fragmentation of gluons and (light) quarks in QCD (light) quarks in QCD scatteringscattering Most often observed interaction at Most often observed interaction at
LHCLHC
Decay of heavy Standard Decay of heavy Standard Model (SM) particles Model (SM) particles Prominent example:Prominent example:
Associated with particle Associated with particle production in Vector Boson production in Vector Boson Fusion (VBF)Fusion (VBF) E.g., HiggsE.g., Higgs
Decay of Beyond Standard Decay of Beyond Standard Model (BSM) particlesModel (BSM) particles E.g., SUSYE.g., SUSY
t bW jjj
t bW l jj
t bW jjj
t bW l jj
qq q q WW Hjj qq q q WW Hjj
electrons or muons
jets
missing transverse
energy
,jets
,leptons
Te f Tjf T ppM p
Where do Jets come from at LHC?C
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0
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9P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Collisions of other partons in Collisions of other partons in the protons generating the the protons generating the signal interactionsignal interaction Unavoidable in hadron-hadron Unavoidable in hadron-hadron
collisionscollisions Independent soft to hard multi-Independent soft to hard multi-
parton interactions parton interactions No real first principle No real first principle calculationscalculations
Contains low pT (non-Contains low pT (non-pertubative) QCDpertubative) QCD
Tuning rather than calculationsTuning rather than calculations Activity shows some correlation Activity shows some correlation
with hard scattering (radiation)with hard scattering (radiation) pTmin, pTmax differencespTmin, pTmax differences
Typically tuned from data in Typically tuned from data in physics generatorsphysics generators
Carefully measured at Carefully measured at TevatronTevatron Phase space factor applied to Phase space factor applied to
LHC tune in absence of dataLHC tune in absence of data One of the first things to be One of the first things to be
measured at LHCmeasured at LHC
Underlying Event
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10P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Δφ
“toward”|Δφ|<60°
“away”|Δφ|>120°
“transverse”60°<|Δφ|<120°
“transverse”60°<|Δφ|<120°
leading jet
Rick Field’s (CDF) view on di-Rick Field’s (CDF) view on di-jet eventsjet events
Collisions of other partons in Collisions of other partons in the protons generating the the protons generating the signal interactionsignal interaction Unavoidable in hadron-hadron Unavoidable in hadron-hadron
collisionscollisions Independent soft to hard multi-Independent soft to hard multi-
parton interactions parton interactions No real first principle No real first principle calculationscalculations
Contains low pT (non-Contains low pT (non-pertubative) QCDpertubative) QCD
Tuning rather than calculationsTuning rather than calculations Activity shows some correlation Activity shows some correlation
with hard scattering (radiation)with hard scattering (radiation) pTmin, pTmax differencespTmin, pTmax differences
Typically tuned from data in Typically tuned from data in physics generatorsphysics generators
Carefully measured at Carefully measured at TevatronTevatron Phase space factor applied to Phase space factor applied to
LHC tune in absence of dataLHC tune in absence of data One of the first things to be One of the first things to be
measured at LHCmeasured at LHCLook at activity (pT, # charged Look at activity (pT, # charged
tracks) as function of leading jet tracks) as function of leading jet pT in transverse regionpT in transverse region
Underlying Event
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11P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
CDF data (√s=1.8 TeV)CDF data (√s=1.8 TeV)
LHC prediction: x2.5 the activity measured at Tevatron!
pT leading jet (GeV)
Nu
mb
er
ch
arg
ed
tra
cks in
tra
nsvers
e r
eg
ion
CDF data: Phys.Rev, D, 65 (2002)
2ln
ln
s
s
PYTHIA
Model depending extrapolation to LHC:
for
for but both agree Tevatron/SppS
PHOJETdata!
Collisions of other partons in Collisions of other partons in the protons generating the the protons generating the signal interactionsignal interaction Unavoidable in hadron-hadron Unavoidable in hadron-hadron
collisionscollisions Independent soft to hard multi-Independent soft to hard multi-
parton interactions parton interactions No real first principle No real first principle calculationscalculations
Contains low pT (non-Contains low pT (non-pertubative) QCDpertubative) QCD
Tuning rather than calculationsTuning rather than calculations Activity shows some correlation Activity shows some correlation
with hard scattering (radiation)with hard scattering (radiation) pTmin, pTmax differencespTmin, pTmax differences
Typically tuned from data in Typically tuned from data in physics generatorsphysics generators
Carefully measured at Carefully measured at TevatronTevatron Phase space factor applied to Phase space factor applied to
LHC tune in absence of dataLHC tune in absence of data One of the first things to be One of the first things to be
measured at LHCmeasured at LHC
Underlying Event
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12P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Multiple Proton Interactions
Expect ~<20> additional proton collisions at Expect ~<20> additional proton collisions at each bunch crossing at LHC design luminosityeach bunch crossing at LHC design luminosity Statistically independentStatistically independent
Actual number at given bunch crossing Poisson distributed Actual number at given bunch crossing Poisson distributed Mostly soft to semi-hard collisionsMostly soft to semi-hard collisions
Very similar dynamics as underlying eventVery similar dynamics as underlying event Generate 100’s-1000’s particles in addition to hard scatterGenerate 100’s-1000’s particles in addition to hard scatter
High occupancy in inner detector is experimentally High occupancy in inner detector is experimentally challengingchallenging
High ionization rate in calorimetersHigh ionization rate in calorimeters Signal collection time versus bunch crossing an issueSignal collection time versus bunch crossing an issue
Experimental handlesExperimental handles Energy flow and track distributions in minimum bias events Energy flow and track distributions in minimum bias events
Help to understand physics features of pile-up eventsHelp to understand physics features of pile-up events Feedback for modelersFeedback for modelers
Multiple primary verticesMultiple primary vertices Explicit reconstruction of MPVs indicates event-by-event pile-Explicit reconstruction of MPVs indicates event-by-event pile-
up activityup activity
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13P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Why Is That Important?
Jet calibration requirements very stringentJet calibration requirements very stringent Systematic jet energy scale Systematic jet energy scale uncertainties to be extremely uncertainties to be extremely well controlledwell controlled
Top mass reconstructionTop mass reconstruction Relative jet energy resolution Relative jet energy resolution requirement requirement
Inclusive jet cross-sectionInclusive jet cross-section Di-quark mass spectra cut-off in SUSYDi-quark mass spectra cut-off in SUSY
Event topology plays a role at 1% level of Event topology plays a role at 1% level of precisionprecision Extra particle production due to event color flowExtra particle production due to event color flow
Color singlet (e.g., Color singlet (e.g., WW) vs color octet (e.g., gluon/quark) jet ) vs color octet (e.g., gluon/quark) jet sourcesource
Small and large angle gluon radiationSmall and large angle gluon radiation Quark/gluon jet differencesQuark/gluon jet differences
Control of underlying event and pile-up contributionsControl of underlying event and pile-up contributions
1 GeV 1%jetT
T jet
Em
m E
1 GeV 1%jetT
T jet
Em
m E
50%3% 3
(GeV)
100%5% 3
(GeV)
E
E
E
50%3% 3
(GeV)
100%5% 3
(GeV)
E
E
E
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14P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
2. Hadronic Signals in ATLAS2. Hadronic Signals in ATLAS
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15P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Total weight : 7000 t
Overall length: 46 m
Overall diameter: 23 m
Magnetic field: 2T solenoid
+ toroid
The ATLAS Detector
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16P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
EM Endcap EMEC
EM Barrel EMB
Hadronic Endcap
ForwardTile Barrel
Tile Extended Barrel
The ATLAS Calorimeters
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17P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Calorimeter Summary
Non-compensating calorimetersNon-compensating calorimeters Electrons generate larger signal than pions depositing the same Electrons generate larger signal than pions depositing the same
energyenergy Typically Typically e/e/ππ ≈ ≈ 1.31.3
High particle stoppingHigh particle stoppingpower over wholepower over wholedetector acceptance detector acceptance ||ηη|<4.9|<4.9 ~26-35 X~26-35 X00 electromagnetic electromagnetic
calorimetrycalorimetry ~ 10 ~ 10 λλ total for hadrons total for hadrons
Hermetic coverageHermetic coverage No significant cracks in No significant cracks in
azimuthazimuth Non-pointing transition between barrel, endcap and forwardNon-pointing transition between barrel, endcap and forward
Small performance penalty for hadrons/jetsSmall performance penalty for hadrons/jets High granularityHigh granularity
6 (barrel)-7 (end-caps) longitudinal samplings6 (barrel)-7 (end-caps) longitudinal samplings ~200,000 independently read-out cells in total~200,000 independently read-out cells in total Pre-samplers in front of barrel and end-capPre-samplers in front of barrel and end-cap
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18P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Signal history in Signal history in calorimeter increases calorimeter increases noisenoise Signal collection in ATLAS Signal collection in ATLAS
10-20 times slower than 10-20 times slower than bunch crossing rate (25 ns)bunch crossing rate (25 ns) Signal history effectively Signal history effectively
adds to noiseadds to noise Baseline suppressed by fast Baseline suppressed by fast
signal shapingsignal shaping Bi-polar shape with net 0 Bi-polar shape with net 0
integralintegral Noise has coherent Noise has coherent
charactercharacter Cell signals linked through Cell signals linked through
past shower developmentspast shower developments
Pile-Up in ATLAS (1)
Prog.Part.Nucl.Phys.60:484-551,2008
Et ~ 58 GeV
Et ~ 81 GeV
without pile-up
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19P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Et ~ 58 GeV
Et ~ 81 GeV
with design luminosity pile-up
Pile-Up in ATLAS (2)
Prog.Part.Nucl.Phys.60:484-551,2008
reading out (digitize) reading out (digitize) 5 samples sufficient!5 samples sufficient!
~450 ns @ 2mm LAr, 1 kV/mm
Cell signal shape in Cell signal shape in
ATLAS LAr Calorimeter ATLAS LAr Calorimeter
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20P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
34 2 110 cm sL 34 2 110 cm sL
8 GeV
0.4R
0.7R
18 GeV
0.1 0.1 R
( ) (GeV)TRMS p
Pile-Up in ATLAS (3)
Prog.Part.Nucl.Phys.60:484-551,2008
Online digital filteringOnline digital filtering Explicit knowledge of Explicit knowledge of
physics pulse shape allows physics pulse shape allows precise reconstruction of precise reconstruction of amplitudeamplitude Needs linear filter Needs linear filter
coefficients and auto-coefficients and auto-correlation matrixcorrelation matrix
Suppresses noise wrt single Suppresses noise wrt single readingreading 1/√2 for 5 samples 1/√2 for 5 samples
First data issuesFirst data issues Pulse-shape and filtering Pulse-shape and filtering
works best at high works best at high ionization ratesionization rates Most complete area Most complete area
cancellation cancellation Initial bunch crossing Initial bunch crossing
50/75/450 ns introduce 50/75/450 ns introduce baselinebaseline Magnitudes depend on Magnitudes depend on
actual bunch spacingactual bunch spacing Increased noise also Increased noise also
possible for some possible for some configurationsconfigurations
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21P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Basic Signal Scales in ATLAS
Cell signalsCell signals Raw data for physics from online system is amplitude, Raw data for physics from online system is amplitude,
time, filter quality, gain selectiontime, filter quality, gain selection Special readout configurations providing 5…32 samples Special readout configurations providing 5…32 samples
possible possible Cell signals are energy, time, quality, and gainCell signals are energy, time, quality, and gain
Energy scale is “electromagnetic” – determined by electron Energy scale is “electromagnetic” – determined by electron testbeams and simulationstestbeams and simulations
All electronic corrections are appliedAll electronic corrections are applied Signal efficiency corrections are applied Signal efficiency corrections are applied
Reduced signals due to HV problems etc.
Towers and clustersTowers and clusters Individual cell signals hard to useIndividual cell signals hard to use
Can be <0 due to noiseCan be <0 due to noise Hard to determine source of signal without Hard to determine source of signal without
context/neighbourhoodcontext/neighbourhood e/h > 1 required specific corrections for hadronic signals
Need to collect cell signals into larger objectsNeed to collect cell signals into larger objects Towers and clustersTowers and clusters
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22P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Impose a regular grid view on Impose a regular grid view on eventevent ΔΔ××ΔφΔφ = 0.1×0.1 = 0.1×0.1 grid grid Motivated by particle Et flow in Motivated by particle Et flow in
hadron-hadronhadron-hadron collisionscollisions Well suited for trigger purposesWell suited for trigger purposes
Collect cells into tower gridCollect cells into tower grid Cells EM scale signals are summed Cells EM scale signals are summed
with geometrical weightswith geometrical weights Depend on cell area containment Depend on cell area containment
ratio ratio Weight = 1 for projective cells of Weight = 1 for projective cells of
equal or smaller than tower sizeequal or smaller than tower size Summing can be selectiveSumming can be selective
See jet input signal discussionSee jet input signal discussion Towers have massless four-Towers have massless four-
momentum representationmomentum representation Fixed direction given by geometrical Fixed direction given by geometrical
grid centergrid center
wcell
1.0
1.0
0.25 0.25
0.25 0.25
η
φ
projective cellsprojective cellsnon-projectivenon-projective
cellscells
0,
0
1 if
1 if
cell
cell cellA A
cellcell
cell
E w E
Aw
A
0,0
1 if
1 if
cell
cell cellA A
cellcell
cell
E w E
Aw
A
2 2 2
, , , , , x y z
x y z
E E p p p p
p p p p
2 2 2
, , , , , x y z
x y z
E E p p p p
p p p p
ATLAS Calorimeter Towers
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FN
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23P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Signal extraction tool Signal extraction tool Attempt reconstruction of Attempt reconstruction of individual particle showersindividual particle showers
Reconstruct 3-dim clusters of Reconstruct 3-dim clusters of cells with correlated signals cells with correlated signals
Use shape of these clusters to Use shape of these clusters to locally calibrate themlocally calibrate them
Explore differences between Explore differences between electromagnetic and hadronic electromagnetic and hadronic shower development and select shower development and select best suited calibrationbest suited calibration
Supress noise with least bias on physics signalsSupress noise with least bias on physics signals Often less than 50% of all cells in an event with “real” signalOften less than 50% of all cells in an event with “real” signal
Some implications of jet environmentSome implications of jet environment Shower overlap cannot always be resolvedShower overlap cannot always be resolved
Clusters represent merged particle showers in dense jetsClusters represent merged particle showers in dense jets Clusters have varying sizes Clusters have varying sizes
No simple jet area as in case of towersNo simple jet area as in case of towers Clusters are mass-less 4-vectors (as towers)Clusters are mass-less 4-vectors (as towers)
No “artificial” mass contribution due to showeringNo “artificial” mass contribution due to showering Issues with IR safety at very small scale insignificantIssues with IR safety at very small scale insignificant
Pile-Up environment triggers split as well as mergePile-Up environment triggers split as well as merge Note that calorimeters themselves are not completely IR safeNote that calorimeters themselves are not completely IR safe
ATLAS Topological Cell Clusters (1)
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24P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Cluster seedingCluster seeding Cluster seed is cell with significant signal above a primary thresholdCluster seed is cell with significant signal above a primary threshold
Cluster growth: direct neighboursCluster growth: direct neighbours Neighbouring cells (in 3-d) with cell signal significance above some basic Neighbouring cells (in 3-d) with cell signal significance above some basic
threshold are collectedthreshold are collected Cluster growth: control of expansionCluster growth: control of expansion
Collect neighbours of neighbours for cells above secondary signal Collect neighbours of neighbours for cells above secondary signal significance threshold significance threshold Secondary threshold lower than primary (seed) threshold Secondary threshold lower than primary (seed) threshold
Cluster splittingCluster splitting Analyze clusters for local signal maxima and split if more than one foundAnalyze clusters for local signal maxima and split if more than one found
Signal hill & valley analysis in 3-dSignal hill & valley analysis in 3-d Final “energy blob” can contain low signal cells Final “energy blob” can contain low signal cells
Cells survive due to significant neighbouring signalCells survive due to significant neighbouring signal Cells inside blob can have negative signalsCells inside blob can have negative signals
ATLAS also studies “TopoTowers”ATLAS also studies “TopoTowers” Use topological clustering as noise suppression tool onlyUse topological clustering as noise suppression tool only Distribute only energy of clustered cells onto tower gridDistribute only energy of clustered cells onto tower grid Motivated by DZero approachMotivated by DZero approach
ATLAS Topological Cell Clusters (2)
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25P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
cluste
r candidate #1
cluste
r candidate #2
#3?
ATLAS Topological Cell Clusters (3)
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26P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Local Hadronic Calibration
Local hadronic energy scale restoration depends on Local hadronic energy scale restoration depends on origin of calorimeter signalorigin of calorimeter signal Attempt to classify energy deposit as electromagnetic or hadronic Attempt to classify energy deposit as electromagnetic or hadronic
from the cluster signal and shapefrom the cluster signal and shape Allows to apply specific corrections and calibrationsAllows to apply specific corrections and calibrations
Local calibration approachLocal calibration approach Use topological cell clusters as signal base for a hadronic energy Use topological cell clusters as signal base for a hadronic energy
scalescale Recall cell signals need context for hadronic calibrationRecall cell signals need context for hadronic calibration
Basic concept is to reconstruct the locally deposited energy from Basic concept is to reconstruct the locally deposited energy from the cluster signal firstthe cluster signal first This is not the particle energyThis is not the particle energy
Additional corrections for energy losses with some correlation to Additional corrections for energy losses with some correlation to the cluster signals and shapes extend the local scopethe cluster signals and shapes extend the local scope True signal loss due to the noise suppression in the cluster algorithm True signal loss due to the noise suppression in the cluster algorithm
(still local)(still local) Dead material losses in front of, or between sensitive calorimeter Dead material losses in front of, or between sensitive calorimeter
volumes (larger scope than local deposit)volumes (larger scope than local deposit) After all corrections, the reconstructed energy is on After all corrections, the reconstructed energy is on
average the isolated particle energyaverage the isolated particle energy E.g., in a testbeamE.g., in a testbeam
But not the jet energy – missing curling tracks, dead material losses But not the jet energy – missing curling tracks, dead material losses without correlated cluster signal,… without correlated cluster signal,…
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27P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Local Scale Sequence
Electronic and readout effects Electronic and readout effects unfolded (nA->GeV calibration)unfolded (nA->GeV calibration)
3-d topological cell clustering 3-d topological cell clustering includes noise suppression and includes noise suppression and establishes basic calorimeter establishes basic calorimeter signal for further processingsignal for further processing
Cluster shape analysis provides Cluster shape analysis provides appropriate classification for appropriate classification for calibration and correctionscalibration and corrections
Cluster character depending Cluster character depending calibration (cell signal weighting for calibration (cell signal weighting for HAD, to be developed for EM?)HAD, to be developed for EM?)
Apply dead material corrections Apply dead material corrections specific for hadronic and specific for hadronic and electromagnetic clusters, resp.electromagnetic clusters, resp.
Apply specific out-of-cluster Apply specific out-of-cluster corrections for hadronic and corrections for hadronic and electromagnetic clusters, resp.electromagnetic clusters, resp.
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28P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
3. Jet Input Objects 3. Jet Input Objects
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29P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Input Objects (1)
Calorimeter signal basedCalorimeter signal based Basic 0.1 x 0.1 calorimeter towersBasic 0.1 x 0.1 calorimeter towers
All cells (~190,000) projected into towersAll cells (~190,000) projected into towers Electromagnetic energy scale signalElectromagnetic energy scale signal No noise suppression but noise cancellation attemptNo noise suppression but noise cancellation attempt
Topological 0.1 x 0.1 calorimeter towersTopological 0.1 x 0.1 calorimeter towers Cells from topological clusters onlyCells from topological clusters only Electromagnetic energy scale signalElectromagnetic energy scale signal Noise suppression like for topological clustersNoise suppression like for topological clusters
Topological calorimeter cell clustersTopological calorimeter cell clusters Electromagnetic and local hadronic scale signalsElectromagnetic and local hadronic scale signals Noise suppressedNoise suppressed
From tracking detectorsFrom tracking detectors Reconstructed tracksReconstructed tracks
Charged particles onlyCharged particles only
From simulationFrom simulation Stable and interacting particles from generators reaching sensitive Stable and interacting particles from generators reaching sensitive
detectorsdetectors
Lab lifetime > 10 psLab lifetime > 10 ps Excludes neutrinos and muons from hard interactionExcludes neutrinos and muons from hard interaction
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30P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Input Objects (2)
from K. Perez, Columbia U.
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31P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jets in the ATLAS Calorimeters
S.D. Ellis, J. Huston, K. Hatakeyama, P. Loch, M. Toennesmann, Prog.Part.Nucl.Phys.60:484-551,2008
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32P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Sequential processSequential process Input signal selectionInput signal selection
Get the best signals out of your detector on a given signal scaleGet the best signals out of your detector on a given signal scale Preparation for jet findingPreparation for jet finding
Suppression/cancellation of “unphysical” signal objects with E<0 (due Suppression/cancellation of “unphysical” signal objects with E<0 (due to noise)to noise)
Possibly event ambiguity resolution (remove reconstructed electrons, Possibly event ambiguity resolution (remove reconstructed electrons, photons, taus,… from detector signal)photons, taus,… from detector signal)
Not done in ATLAS before jet reconstruction! Pre-clustering to speed up reconstruction (not needed anymore)Pre-clustering to speed up reconstruction (not needed anymore)
Jet findingJet finding Apply your jet finder of choiceApply your jet finder of choice
All implementations from FastJet and SISCone available Default is AntiKt4 with R = 0.4Default is AntiKt4 with R = 0.4
Reference is legacy ATLAS seeded fixed cone Narrow jets least affected by pile-up
Jet calibrationJet calibration Depending on detector input signal definition, jet finder choices, Depending on detector input signal definition, jet finder choices,
references…references… Default calibration uses cell weightsDefault calibration uses cell weights
Jet selectionJet selection Apply cuts on kinematics etc. to select jets of interest or significanceApply cuts on kinematics etc. to select jets of interest or significance
ObjectiveObjective Reconstruct particle level featuresReconstruct particle level features
Test models and extract physicsTest models and extract physics
ATLAS Jet Reconstruction
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33P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Monte Carlo Jet Calibration
Typical Monte Carlo based normalizationTypical Monte Carlo based normalization Match particle level jets with detector jets in simple topologies Match particle level jets with detector jets in simple topologies
(fully simulated QCD di-jets)(fully simulated QCD di-jets) Use same specific jet definition for bothUse same specific jet definition for both Match defined by maximum angular distanceMatch defined by maximum angular distance
Can include isolation requirements
Determine calibration function parameters using truth particle jet Determine calibration function parameters using truth particle jet energy constraintenergy constraint Fit calibration parameters such that relative energy resolution is bestFit calibration parameters such that relative energy resolution is best
Include whole phase space into fit (flat in energy)
Correct residual non-linearities by jet energy scale correction Correct residual non-linearities by jet energy scale correction functionfunction Numerical inversion technique applied hereNumerical inversion technique applied here
Magnitudes of calibrations and corrections depend on Magnitudes of calibrations and corrections depend on signal choicessignal choices Electromagnetic energy scale signals require large corrections Electromagnetic energy scale signals require large corrections
while particle level or local hadronic signal have much less while particle level or local hadronic signal have much less correctionscorrections Effect on systematic errorsEffect on systematic errors
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34P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Jet Cell Weights
Cell signal weightingCell signal weighting Statistically determined cell signal Statistically determined cell signal weights try to compensate for weights try to compensate for e/h>1 in jet contexte/h>1 in jet context
Motivated by H1 cell weightingMotivated by H1 cell weighting High cell signal density indicatesHigh cell signal density indicates on average electromagnetic on average electromagnetic signal originsignal origin
Ideally weight = 1
Low cell signal indicates hadronic depositLow cell signal indicates hadronic deposit Weight > 1
Cell weights are determined as function of cell signal density and Cell weights are determined as function of cell signal density and locationlocation Use truth jet matching in fully simulated QCD di-jet events Use truth jet matching in fully simulated QCD di-jet events Crack regions not included in fitCrack regions not included in fit
Residual jet energy scale corrections – see next slidesResidual jet energy scale corrections – see next slides
2
2
1
( , )cells
rec true
jets trueN
rec i i i i i ii
E E
E
E w E V X E
2
2
1
( , )cells
rec true
jets trueN
rec i i i i i ii
E E
E
E w E V X E
Jets
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35P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Cell Weight Calibration For Jets
Cell signal weighting Cell signal weighting functions do not restore functions do not restore jet energy scale for all jet energy scale for all jetsjets Crack regions not included Crack regions not included
in fitsin fits Only on jet context used for Only on jet context used for
fitting weightsfitting weights Cone jets with R=0.7Cone jets with R=0.7
Only one calorimeter signal Only one calorimeter signal definition used for weight definition used for weight fitsfits CaloTowers CaloTowers
Additional response Additional response corrections applied to corrections applied to restore linearityrestore linearity Non-optimal resolution for Non-optimal resolution for
other than reference jet other than reference jet samples can be expectedsamples can be expected
Changing physics Changing physics environment not environment not explicitly corrected explicitly corrected Absolute precision limitationAbsolute precision limitation
, 3 , 0
, , ,
( , ) , , ,
, , ,
calo calo calo calox y z
cell cell cell cellcell cell x y z
cells
calo caloDM t EMB t Tile
calo calo calo calo calo calo calo caloT x T y T z
E p p p
w X E p p p
E E
p p p p p p p p
, 3 , 0
, , ,
( , ) , , ,
, , ,
calo calo calo calox y z
cell cell cell cellcell cell x y z
cells
calo caloDM t EMB t Tile
calo calo calo calo calo calo calo caloT x T y T z
E p p p
w X E p p p
E E
p p p p p p p p
Jets
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36P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Cell Weight Calibration For Jets
Cell signal weighting Cell signal weighting functions do not restore functions do not restore jet energy scale for all jet energy scale for all jetsjets Crack regions not included Crack regions not included
in fitsin fits Only on jet context used for Only on jet context used for
fitting weightsfitting weights Cone jets with R=0.7Cone jets with R=0.7
Only one calorimeter signal Only one calorimeter signal definition used for weight definition used for weight fitsfits CaloTowers CaloTowers
Additional response Additional response corrections applied to corrections applied to restore linearityrestore linearity Non-optimal resolution for Non-optimal resolution for
other than reference jet other than reference jet samples can be expectedsamples can be expected
Changing physics Changing physics environment not environment not explicitly corrected explicitly corrected Absolute precision limitationAbsolute precision limitation
, , ,
( , ) , , ,
final final final finalx y z
calo calo calo calo calo caloT x y z
E p p p
f p E p p p
, , ,
( , ) , , ,
final final final finalx y z
calo calo calo calo calo caloT x y z
E p p p
f p E p p p
Jets
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37P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS Cell Weight Calibration For Jets
Cell signal weighting Cell signal weighting functions do not restore functions do not restore jet energy scale for all jet energy scale for all jetsjets Crack regions not included Crack regions not included
in fitsin fits Only one jet context used Only one jet context used
for fitting weightsfor fitting weights Cone jets with R=0.7Cone jets with R=0.7
Only one calorimeter signal Only one calorimeter signal definition used for weight definition used for weight fitsfits CaloTowers CaloTowers
Additional response Additional response corrections applied to corrections applied to restore linearityrestore linearity Non-optimal resolution for Non-optimal resolution for
other than reference jet other than reference jet samples can be expectedsamples can be expected
Changing physics Changing physics environment not environment not explicitly corrected explicitly corrected Absolute precision limitationAbsolute precision limitation
Response for jets in ttbar(same jet finder as used fordetermination of calibrationfunctions with QCD events)
Jets
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38P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Final Jet Energy Scale Calibration
Jet energy scale (JES) for first dataJet energy scale (JES) for first data Fully Monte Carlo based calibrations hard to validate quickly Fully Monte Carlo based calibrations hard to validate quickly
with initial datawith initial data Too many things have to be right, including underlying event Too many things have to be right, including underlying event
tunes, pile-up activity, etc.tunes, pile-up activity, etc. Mostly a generator issue in the beginningMostly a generator issue in the beginning
Need flat response and decent energy resolution for jets as Need flat response and decent energy resolution for jets as soon as possiblesoon as possible Data driven scenario a la DZero implementedData driven scenario a la DZero implemented
Additional jet by jet correctionsAdditional jet by jet corrections Interesting ideas to use all observable signal features for Interesting ideas to use all observable signal features for
jets to calibratejets to calibrate Geometrical momentsGeometrical moments Energy sharing in calorimetersEnergy sharing in calorimeters
Concerns about stability and MC dependence to be Concerns about stability and MC dependence to be understoodunderstood Can consider e.g. truncated moments using only prominent Can consider e.g. truncated moments using only prominent
constituents for stable signalconstituents for stable signal
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39P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009ATLAS JES Correction Model for First Data
optional
data driven
MC
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40P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
4. Jet Reconstruction Performance4. Jet Reconstruction Performance
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41P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Performance Evaluations (1)
Jet performance evaluationJet performance evaluation Proof of success for each methodProof of success for each method
Closure tests applied to calibration data sourceClosure tests applied to calibration data source
Strong indications that one jet reconstruction approach is Strong indications that one jet reconstruction approach is not sufficientnot sufficient Evaluation needs to be extended to different final statesEvaluation needs to be extended to different final states Systematic errors and corrections for alternative jet finders Systematic errors and corrections for alternative jet finders
and configurations need to be evaluatedand configurations need to be evaluated
G. Salam, talk at ATLAS Hadronic Calibration Workshop, Tucson, Arizona, USA, March 2009
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42P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Performance Evaluations (2)
Jet signal linearity and resolutionJet signal linearity and resolution Closure tests for calibration determinationClosure tests for calibration determination
Ultimate precision and resolution for given method applied to Ultimate precision and resolution for given method applied to calibration samplecalibration sample
Response comparisons for different signal definitionsResponse comparisons for different signal definitions Need to reduce exploration phase spaceNeed to reduce exploration phase space Real data needed for final decisionReal data needed for final decision
E.g. towers vs clusters, calibration scheme
Jet Energy Scale (JES) stabilityJet Energy Scale (JES) stability Typically better than 2% with respect to signal linearity in Typically better than 2% with respect to signal linearity in
closure testsclosure tests Calibration approaches are stableCalibration approaches are stable
Signal uniformity within the same average deviationsSignal uniformity within the same average deviations Resolution goal is achievable with studied calibration Resolution goal is achievable with studied calibration
approachesapproaches See next slideSee next slide
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43P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Energy Resolution
98
63
26 98
63
26
1123728 299
111
41
1123728 299
111
41
(GeV) for 88 1
(GeV) for 1020 1
0
2
7 GeV
26 GeVt
t j t
jet
e
p E
p E
Very preliminary!Very preliminary!
Older evaluation!Older evaluation!
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44P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Data Driven Evaluation
Photon+jet(s)Photon+jet(s) Well measured electromagnetic Well measured electromagnetic
system balances jet responsesystem balances jet response Central value theoretical Central value theoretical
uncertainty ~2% limits precisionuncertainty ~2% limits precision Due to photon isolation
requirements
But very good final state for But very good final state for evaluating calibrationsevaluating calibrations
Can test different correction levels Can test different correction levels in factorized calibrationsin factorized calibrations E.g., local hadronic calibration in E.g., local hadronic calibration in
ATLASATLAS
Limited pT reach for 1-2% Limited pT reach for 1-2% precisionprecision 25->300 GeV within 100 pb25->300 GeV within 100 pb-1-1
Z+jet(s)Z+jet(s) Similar idea, but less initial Similar idea, but less initial
statisticsstatistics Smaller reach but less backgroundSmaller reach but less background
Jets
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45P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Data Driven Evaluation
Photon+jet(s)Photon+jet(s) Well measured electromagnetic Well measured electromagnetic
system balances jet responsesystem balances jet response Central value theoretical Central value theoretical
uncertainty ~2% limits precisionuncertainty ~2% limits precision Due to photon isolation
requirements
But very good final state for But very good final state for evaluating calibrationsevaluating calibrations
Can test different correction levels Can test different correction levels in factorized calibrationsin factorized calibrations E.g., local hadronic calibration in E.g., local hadronic calibration in
ATLASATLAS
Limited pT reach for 1-2% Limited pT reach for 1-2% precisionprecision 25->300 GeV within 100 pb-125->300 GeV within 100 pb-1
Z+jet(s)Z+jet(s) Similar idea, but less initial Similar idea, but less initial
statisticsstatistics Smaller reach but less backgroundSmaller reach but less background
Jets
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46P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009W Mass Spectroscopy
In-situ calibration In-situ calibration validation handlevalidation handle Precise reference in ttbar Precise reference in ttbar
eventsevents Hadronically decaying W-Hadronically decaying W-
bosonsbosons
Jet calibrations should Jet calibrations should reproduce W-massreproduce W-mass Note color singlet sourceNote color singlet source No color connection to rest of No color connection to rest of
collision – different underlying collision – different underlying event as QCDevent as QCD
Also only light quark jet Also only light quark jet referencereference
Expected to be sensitive to jet Expected to be sensitive to jet algorithmsalgorithms Narrow jets perform better – Narrow jets perform better –
as expectedas expected
raw signal
Jets
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47P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jets Not From Hard Scatter
Dangerous background for W+n jets cross-Dangerous background for W+n jets cross-sections etc.sections etc.
Lowest pT jet of final state can be faked or Lowest pT jet of final state can be faked or misinterpreted as coming from underlying event misinterpreted as coming from underlying event or multiple interactionsor multiple interactions
Extra jets from UE are hard to handleExtra jets from UE are hard to handle No real experimental indication of jet sourceNo real experimental indication of jet source
Some correlation with hard scattering?Some correlation with hard scattering? Jet area?Jet area? No separate vertexNo separate vertex
Jet-by-jet handle for multiple proton Jet-by-jet handle for multiple proton interactionsinteractions
Classic indicator for multiple interactions is Classic indicator for multiple interactions is number of reconstructed vertices in eventnumber of reconstructed vertices in event
Tevatron with RMS(z_vertex) ~ 30 cmTevatron with RMS(z_vertex) ~ 30 cm LHC RMS(z_vertex) ~ 8 cmLHC RMS(z_vertex) ~ 8 cm
If we can attach vertices to reconstructed jets, we If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard can in principle identify jets not from hard scatteringscattering
Limited to pseudorapidities within 2.5!Limited to pseudorapidities within 2.5!
Track jetsTrack jets Find jets in recon-Find jets in recon- structed tracksstructed tracks
~60% of jet pT, ~60% of jet pT, with RMS ~0.3 – with RMS ~0.3 – not a good not a good kinematic estimatorkinematic estimator
Dedicated algorithmDedicated algorithm Cluster track jets in Cluster track jets in pseudo-rapidity, azimuth, pseudo-rapidity, azimuth, and delta(Zand delta(ZVertexVertex))
Match track and Match track and calorimeter jetcalorimeter jet
Also helps response!Also helps response!
Jets
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48P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
6. Conclusions & Outlook6. Conclusions & Outlook
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49P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Conclusions
This was a mere snapshotThis was a mere snapshot Jet reconstruction at ATLAS deserves a bookJet reconstruction at ATLAS deserves a book
Complex environment, complex signals, lots of information Complex environment, complex signals, lots of information content in ATLAS (and CMS) eventscontent in ATLAS (and CMS) events
First data jet reconstruction and calibration First data jet reconstruction and calibration strategy in placestrategy in place Includes simulation and data inputIncludes simulation and data input
Emphasis on “data driven”Emphasis on “data driven” Expect to establish flat jet response for first physics quicklyExpect to establish flat jet response for first physics quickly
Discussion on how to establish initial systematic uncertainties Discussion on how to establish initial systematic uncertainties just startedjust started Some ideas exist but procedures need to be ironed out betterSome ideas exist but procedures need to be ironed out better Still on track for first useful collision dataStill on track for first useful collision data
We are looking beyond obvious jet performance We are looking beyond obvious jet performance variablesvariables Jet shapes are considered for refined JES calibrationJet shapes are considered for refined JES calibration
Jet-by-jet correctionsJet-by-jet corrections Experimental sensitivity to unfold jet substructure exploredExperimental sensitivity to unfold jet substructure explored
Needs more studies with real dataNeeds more studies with real data Discovery tool for boosted heavy particles!Discovery tool for boosted heavy particles!
We are waiting for collision data!We are waiting for collision data!
Jets
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50P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Some BackupSome Backup
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51P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Electromagnetic Calorimetry
Highly segmented Highly segmented lead/liquid argon accordionlead/liquid argon accordion No azimuthal cracksNo azimuthal cracks 3 depth segments3 depth segments
+ pre-sampler (limited + pre-sampler (limited coverage)coverage)
Strip cells in 1Strip cells in 1stst layer layer Very high granularity in pseudo-Very high granularity in pseudo-
rapidity rapidity
Deep cells in 2Deep cells in 2ndnd layer layer High granularity in both High granularity in both
directionsdirections
Shallow cells in 3Shallow cells in 3rdrd layer layer
0.003 0.1
0.025 0.025
0.05 0.025
Electromagnetic BarrelElectromagnetic BarrelElectromagnetic BarrelElectromagnetic Barrel
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52P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Tile calorimeterTile calorimeter Iron/scintillator tiled readoutIron/scintillator tiled readout 3 depth segments3 depth segments
Quasi-projective readout cellsQuasi-projective readout cells First two layers:First two layers:
Third layerThird layer
Very fast light Very fast light
collectioncollection ~50 ns~50 ns Dual fiber Dual fiber
readout for each readout for each
channelchannel
0.1 0.1 0.1 0.1
0.2 0.1 0.2 0.1
Hadronic Calorimetry
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53P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
EndCap Calorimeters
0.025 0.025 2.5, middle layer
0.1 0.1 2.5 3.2
0.025 0.025 2.5, middle layer
0.1 0.1 2.5 3.2
0.1 0.1 2.5
0.2 0.2 2.5 3.2
0.1 0.1 2.5
0.2 0.2 2.5 3.2
Electromagnetic “Spanish Electromagnetic “Spanish Fan” accordionFan” accordion Highly segmented with up to Highly segmented with up to
three longitudinal segmentsthree longitudinal segments
Hadronic liquid Hadronic liquid argon/copper argon/copper calorimetercalorimeter Parallel plate Parallel plate
designdesign Four longitudinal Four longitudinal
segmentssegments Quasi-projective Quasi-projective
cellscells
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54P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
FCal1FCal1
FCal2FCal2
FCal3FCal3
Forward Calorimeters
Design featuresDesign features Compact absorbersCompact absorbers
Small showersSmall showers Tubular thin gap electrodesTubular thin gap electrodes
Suppress positive charge build-up Suppress positive charge build-up (Ar+) in high ionization rate (Ar+) in high ionization rate environmentenvironment
Stable calibrationStable calibration Rectangular non-projective readout Rectangular non-projective readout
cellscells
Electromagnetic FCal1Electromagnetic FCal1 Liquid argon/copperLiquid argon/copper
Gap ~260 Gap ~260 μμmm Hadronic FCal2Hadronic FCal2
Liquid argon/tungstenLiquid argon/tungsten Gap ~375 Gap ~375 μμmm
Hadronic FCal3Hadronic FCal3 Liquid argon/tungstenLiquid argon/tungsten
Gap ~500 Gap ~500 μμmm
0.2 0.2 0.2 0.2
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55P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Tower Building(Δη×Δφ=0.1×0.1, non-discriminant)
CaloCells(em scale)
CaloTowers(em scale)
Calorimeter Jets(em scale)
Jet Finding(cone R=0.7,0.4; kt)
Jet Based Hadronic Calibration(“H1-style” cell weighting in jets etc.)
Calorimeter Jets(fully calibrated had scale)
Physics Jets(calibrated to particle level)
Jet Energy Scale Corrections(noise, pile-up, algorithm effects, etc.)
Refined Physics Jet(calibrated to interaction level)
In-situ Calibration(underlying event, physics environment, etc.)
ProtoJets(E>0,em scale)
Tower Noise Suppression(cancel E<0 towers by re-summation)
Sum up electromagnetic scale calorimeter cell signals Sum up electromagnetic scale calorimeter cell signals into towersinto towers Fixed grid of Fixed grid of ΔΔηη x x ΔΔφφ = 0.1 x 0.1 = 0.1 x 0.1 Non-discriminatory, no cell suppressionNon-discriminatory, no cell suppression Works well with pointing readout geometriesWorks well with pointing readout geometries
Larger cells split their signal between towers according to the overlap Larger cells split their signal between towers according to the overlap area fractionarea fraction
Tower noise suppressionTower noise suppression Some towers have net negative signalsSome towers have net negative signals Apply “nearest neighbour tower recombination”Apply “nearest neighbour tower recombination”
Combine negative signal tower(s) with nearby positive signal towers Combine negative signal tower(s) with nearby positive signal towers until sum of signals > 0until sum of signals > 0
Remove towers with no nearby neighboursRemove towers with no nearby neighbours
Towers are “massless” pseudo-particlesTowers are “massless” pseudo-particles Find jetsFind jets
Note: towers have signal on electromagnetic energy scaleNote: towers have signal on electromagnetic energy scale Calibrate jetsCalibrate jets
Retrieve calorimeter cell signals in jetRetrieve calorimeter cell signals in jet Apply signal weighting functions to these signalsApply signal weighting functions to these signals Recalculate jet kinematics using these cell signalsRecalculate jet kinematics using these cell signals
Note: there are cells with negative signals!Note: there are cells with negative signals!
Apply final correctionsApply final corrections
CaloTower Jets
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56P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Topological Clustering(includes noise suppression)
CaloCells(em scale)
Calorimeter Jets(em scale)
Cluster Classification(identify em type clusters)
Jet Finding(cone R=0.7,0.4; kt)
Out Of Cluster Corrections(hadronic & electromagnetic)Jet Based Hadronic Calibration
(“H1-style” cell weighting in jets etc.)
Jet Finding(cone R=0.7,0.4; kt)
Jet Energy Scale Corrections(noise, pile-up, algorithm effects, etc.)
Refined Physics Jet(calibrated to interaction level)
In-situ Calibration(underlying event,
physics environment, etc.)
Hadronic Cluster Calibration(apply cell signal weighting)
Dead Material Correction(hadronic & eleectromagentic)
CaloClusters(em scale, classified)
CaloClusters(locally calibrated had scale)
CaloClusters(hadronic scale)
CaloClusters(had scale+DM)
Calorimeter Jets(partly calibrated/corrected)
Jet Finding(cone R=0.7,0.4; kt)
CaloClusters(em scale)
Calorimeter Jets(fully calibrated had scale)
Physics Jets(calibrated to particle level)
TopoCluster Jets
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57P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
Tower Building(Δη×Δφ=0.1×0.1, non-discriminant)
CaloCells(em scale, selected)
CaloTowers(em scale)
Calorimeter Jets(em scale)
Jet Finding(cone R=0.7,0.4; kt)
Jet Based Hadronic Calibration(“H1-style” cell weighting in jets etc.)
Calorimeter Jets(fully calibrated had scale)
Physics Jets(calibrated to particle level)
Jet Energy Scale Corrections(noise, pile-up, algorithm effects, etc.)
Refined Physics Jet(calibrated to interaction level)
In-situ Calibration(underlying event,
physics environment, etc.)
Topological Clustering(includes noise suppression)
CaloCells(em scale)
CaloClusters(em scale)
Extract Cells In Clusters(excludes noisy cells)
Apply noise Apply noise suppression to tower suppression to tower jetsjets Topological clustering is Topological clustering is
used as a noise used as a noise suppression tool onlysuppression tool only
Similar to DZero Similar to DZero approachapproach
New implementationNew implementation Only in ESD context so Only in ESD context so
farfar Working on schema to Working on schema to
bring these jets into the bring these jets into the AODAOD Including constituentsIncluding constituents
Allows comparisons Allows comparisons for tower and cluster for tower and cluster jets with similar jets with similar noise contributionnoise contribution Should produce rather Should produce rather
similar jets than tower similar jets than tower jets at better jets at better resolutionresolution
Less towers per jetLess towers per jet
TopoTower Jets
CERN-OPEN-2008-020
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58P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Calibration Flow
Electromagnetic Scale J et
Electromagnetic Scale Cells
Apply WeightsDead Material
Correction
Recombine
Final Energy Scale J et
Apply Final Correction
cells in EMB3/Tile0all cells
Retreive Cells
Local Hadronic Scale J et
Final Energy Scale J et
Apply Final Correction
Lots of work in calorimeter domain!
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59P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Data Driven JES Corrections (1)
PileUp subtractionPileUp subtraction Goal:Goal:
Correct in-time and residual out-of-time Correct in-time and residual out-of-time pile-up contribution to a jet on averagepile-up contribution to a jet on average
Tools:Tools: Zero bias (random) events, minimum bias Zero bias (random) events, minimum bias
eventsevents
Measurement:Measurement: Et density in Et density in ΔΔ××ΔφΔφ bins as function of bins as function of # vertices# vertices TopoCluster feature (size, average TopoCluster feature (size, average energy as function of depth) changesenergy as function of depth) changes as function of # vertices as function of # vertices
Remarks:Remarks: Uses expectations from the average Et flow Uses expectations from the average Et flow
for a given instantaneous luminosityfor a given instantaneous luminosity Instantaneous luminosity is measured by Instantaneous luminosity is measured by
the # vertices in the eventthe # vertices in the event Requires measure of jet size (AntiKt Requires measure of jet size (AntiKt
advantage)advantage)
Concerns:Concerns: Stable and safe determination of averageStable and safe determination of average
UEUE TE UEUE TE
coshUEoffset UE jet jetE A coshUEoffset UE jet jetE A
DD
DD
Determination of the Absolute Jet Energy Scale in the D0 Calorimeters. NIM A424, 352 (1999)
Note that magnitude of correction depends on
calorimeter signal processing!
Note that magnitude of correction depends on
calorimeter signal processing!
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60P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Data Driven JES Corrections (2)
Absolute responseAbsolute response Goal:Goal:
Correct for energy (pT) dependent jet responseCorrect for energy (pT) dependent jet response
Tools: Tools: Direct photons, Z+jet(s),…Direct photons, Z+jet(s),…
Measurement:Measurement: pT balance of well calibrated system (photon, Z) pT balance of well calibrated system (photon, Z) against jet in central regionagainst jet in central region
Remarks:Remarks: Usually uses central reference and central jets (region of flat reponse)Usually uses central reference and central jets (region of flat reponse)
Concerns:Concerns: Limit in precision and estimates for systematics w/o well understood Limit in precision and estimates for systematics w/o well understood
simulations not clearsimulations not clear
t
jett t
pt
p pf
p
t
jett t
pt
p pf
p
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61P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Data Driven JES Corrections (3)
Direction response correctionsDirection response corrections Goal:Goal:
Equalize response as function of jet Equalize response as function of jet (pseudo)rapidity(pseudo)rapidity
Tools:Tools: QCD di-jetsQCD di-jets Direct photonsDirect photons
Measurement:Measurement: Di-jet pT balance uses Di-jet pT balance uses reference jet in well calibratedreference jet in well calibrated (central) region to correct (central) region to correct second jet further awaysecond jet further away Measure hadronic response Measure hadronic response variations as function of the jet variations as function of the jet direction with the missing Et direction with the missing Et projection fraction (MPF) method projection fraction (MPF) method
Remarks:Remarks: MPF only needs jet for direction MPF only needs jet for direction
referencereference Bi-sector in di-jet balance explores Bi-sector in di-jet balance explores
different sensitivitiesdifferent sensitivities Concerns:Concerns:
MC quality for systematic uncertaunty MC quality for systematic uncertaunty evaluationevaluation
Very different (jet) energy scales Very different (jet) energy scales between reference and probed jetbetween reference and probed jet
uncalibrated
calo signals
calot t
tjetcalo
t
p p
pR
p
calo signals
calot t
tjetcalo
t
p p
pR
p
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62P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Mass Sensitivity
change o
f m
ass
log10(least biased reconstructed mass/GeV)
QCD kT jets, D = 0.6
ATLAS MC(preliminary)
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63P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009
We expected clusters to represent We expected clusters to represent individual particlesindividual particles Cannot be perfect in busy jet Cannot be perfect in busy jet
environment!environment! Shower overlap in finite calorimeter Shower overlap in finite calorimeter
granularitygranularity
Some resolution power, thoughSome resolution power, though Much better than for tower jets!Much better than for tower jets!
~1.6:1 particles:clusters in central ~1.6:1 particles:clusters in central regionregion This is an average estimator subject to This is an average estimator subject to
large fluctuationslarge fluctuations
~1:1 in endcap region~1:1 in endcap region Best match of readout granularity, Best match of readout granularity,
shower size and jet particle energy flowshower size and jet particle energy flow Happy coincidence, not a design feature Happy coincidence, not a design feature
of the ATLAS calorimeter!of the ATLAS calorimeter!
Jet CompositionS.D
. Ellis e
t al., P
rog.P
art.N
ucl.P
hys.6
0:4
84
-55
1,2
00
8
Jets
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64P. LochP. Loch
U of ArizonaU of Arizona
Aug. 27, 2009Aug. 27, 2009Jet Substructure Mass too complex?Mass too complex?
Can be too sensitive to small Can be too sensitive to small signals in jetssignals in jets UE, pile-up, other noiseUE, pile-up, other noise
Use YSplitter to detect Use YSplitter to detect substructuresubstructure Determines scale y for splitting Determines scale y for splitting
a giving jet into 2,3,… subjects, a giving jet into 2,3,… subjects, as determined by yas determined by ycutcut, from, from
More stable as only significant More stable as only significant constituents are used ?constituents are used ?
At least additional information At least additional information to massto mass
Other option:Other option: Look at mass of 2…n hardest Look at mass of 2…n hardest
constituents (Ben Lillie,ANL) constituents (Ben Lillie,ANL)
jetcut Ty y p
Not very sensitive to calorimeter signal details!