1. Jet Reconstruction in ATLAS
Peter Loch
University of Arizona
Tucson, Arizona, USA
loch-at-physics.arizona.edu

2. Outline Jets at LHC Jet reconstruction in ATLAS More from jets at LHC
Brief overview on sources for jets and jet features at LHC
Physics environment: Underlying Event and Pile-up
Jet reconstruction in ATLAS
Jet finders
Tower and cluster jets
Jet calibration strategies
In-situ calibration
More from jets at LHC
Jet reconstruction performance
Mass reconstruction and jet substructure analysis
Jet shapes
Summary & Outlook

3. Jets at LHC New kinematic regime for jet physics
W. Stirling, LHCC Workshop “Theory of LHC Processes” (1998)
*annotation from J. Huston, ATLAS Standard Model WG Meeting (Feb. 2004)
New kinematic regime for jet physics
Jets can be much harder
Jets get more narrow in general (kinematic effect ~αs)
Higher energies to be contained in calorimeters
Jet reconstruction challenging
Physics requirements typically 1% jet energy scale uncertainty
top mass measurement in ttbar
LHC is a top factory!
hadronic final states in at the end of long decay chains in SUSY
Quality takes time
Previous experiments needed up to 10 years of data taking to go from ~4% down to ~1%
Can often not be achieved for all kinds of jets and in all physics environments

4. Jets from QCD Processes
Jets at low pT most likely produced by gluon fusion
Large phase space for radiation
Expectation are multi-jet final states even for 2→2 processes
More likely quark jets at higher pT
Less radiation (Sudakov suppression)
Less jets in events
Narrower jets (again)
Large kinematic range
pT range GeV/c
Di-jet mass reach several TeV/c2
Multitude of jet flavours
Expect corresponding variety of jet shapes with possibly specific calibrations!

5. Physics Environments @ LHC
Underlying event
Refers to multiple interactions between the two colliding protons
Typically correlated with hard scatter
Increased activity compared to Tevatron
more phase space
A.Moraes, HERA-LHC Workshop,
DESY, March 2007
LHC prediction: x2.5 the activity measured at Tevatron!
Number charged tracks in transverse region
independent of luminosity → present at day 1 at LHC!
CDF data (√s=1.8 TeV)
CDF data: Phys.Rev, D, 65 (2002)
Rick Field’s (CDF) view on di-jet events
pT leading jet (GeV)

6. Pile-up Large pp total cross-section (~75mb)
Et ~ 58 GeV
Et ~ 81 GeV
no pile-up added
LHC design luminosity pile-up added
Large pp total cross-section (~75mb)
~23 “minimum bias” (soft to medium hard) collisions between protons in the same bunch in addition to triggered hard scatter
@ 1034 cm-2 s-1
Poisson distributed
Similar dynamics as UE
Statistically independent
No correlation to hard scatter!
Generate lots of additional particles in addition to the underlying event
~370 particles/unit rapidity per bunch crossing (3700 within ATLAS)
~1,800 charged tracks in ATLAS/bunch crossing
High crossing rate at LHC (40 MHz)
Calorimeter signals typically to slow
Short signal shaping, bi-polar shaping function (ATLAS)
Long signal history (~ ns) generates “out-of-time” pile-up
Canceling area → integrated effect 0
Careful, only true in limit of continuous collisions
Can be treated as noise in calorimeter
Asymmetric!
P. Savard et al., ATLAS-CAL-NO 084/1996
R = 0.7

7. Experimenter’s View on Jets
longitudinal energy leakage
detector signal inefficiencies (dead channels, HV…)
pile-up noise from (off- and in-time) bunch crossings
electronic noise
calo signal definition (clustering, noise suppression ,…)
dead material losses (front, cracks, transitions…)
detector response characteristics (e/h ≠ 1)
jet reconstruction algorithm efficiency
jet reconstruction algorithm efficiency
added tracks from in-time (same trigger) pile-up event
added tracks from underlying event
lost soft tracks due to magnetic field
physics reaction of interest (interaction or parton level)
We like to factorize the calibration and corrections dealing with these effects as much as possible!

8. Theoretical Requirements for Jet Finders
Infrared safety
Absence of additional radiation splits jets
Presence of radiation merges jets
Collinear safety
Split seeds
infrared sensitivity
(soft gluon radiation merges jets)
collinear sensitivity (1)
(sensitive to Et ordering of seeds)
collinear sensitivity (2)
(signal split into two towers below threshold)

9. Experimental Requirements for Jet Finders
Detector technology independence
Minimal contributions to spatial and energy resolution
Insignificant effects of detector environment
Noise
Dead material
Cracks
Easy to calibrate
Well…
Environment independence
Stability with changing luminosity
Identify all physically interesting jets from energetic partons in pertubative QCD (pQCD)
High reconstruction efficiency
Implementation
Fully specified
All selections and other configurations known
Efficient use of computing sources

10. Popular Jet Algorithms in ATLAS
Seeded cone
Place cone with radius R around seed
pT > 1 GeV
Collect all particles in cone
Re-calculate energy and direction of cone
4-momentum recombination
Find more particle in new cone
Stop until no more particles to be found
Stable solution
Particles can be shared between jets
Is not infrared safe
Needs split & merge (50% threshold)
May miss signficant energy
Dark jets
Recursive recombination (kT)
Calculate for all particles i and pairs ij :
Combine any two pairs to jet if:
Else remove i from list
Is a jet
Calculate new combinations
Stop when all particles declared jets
Each particle is part of one jet only (exclusive assignment)
Infrared safe

11. Jet Finders in ATLAS Alternative applications:
CDF mid-point, Cambridge/Aachen recursive recombination (0th order kT), “optimal jet finder” (event shape fit)
More options: move to FastJet libraries
CMS, theory
No universal configuration or jet finder
Narrow jets
W->jj in ttbar, some SUSY
Wider jets
Inclusive jet cross-section, QCD mW
N.Godbhane, JetRec June 2006
Algorithm
Rcone D
Clients
Seeded Cone
EtSeed = 1 GeV, fS/M = 0.5
0.4
W mass spectroscopy, top physics, SUSY
Kt (FastKt)
0.7
QCD, jet cross-sections
0.6
P.-A. Delsart, June 2006

12. The ATLAS Detector

13. ATLAS Calorimeters Electromagnetic Calorimeters:
Liquid Argon/Pb accordion structure;
highly granular readout (~170,000 channels);
≤ Δη ≤ 0.05, ≤ Δφ ≤ 0.1;
2-3 longitudinal samplings;
~24-26 X0 deep
covers |η|<3.2, presampler up to |η|<1.8;
Central Hadronic Calorimeters
Scintillator/Fe in tiled readout;
Δη x Δφ = 0.1 x 0.1
3 longitudinal samplings,
covers |η|<1.7;
EndCap Hadronic Calorimeters
Liquid Argon/Cu parallel plate absorber structure;
Δη x Δφ = 0.1 x 0.1 (1.5<|η|<2.5),
Δη x Δφ = 0.2 x 0.2 (2.5<|η|<3.2);
4 samplings;
Forward Calorimeters
Liquid Argon/Cu or W absorbers with tubular electrodes in non-projective geometry;
Δη x Δφ ≈ 0.2 x 0.2 (3.2<|η|<4.9)
3 samplings;
ATLAS Calorimeters
Electromagnetic Liquid Argon
Calorimeters
Tile Calorimeters
η=1.475
η=1.8
η=3.2
Forward Liquid Argon Calorimeters
Hadronic Liquid Argon EndCap Calorimeters

14. ATLAS Calorimeter Details
Cryostat walls
(warm/cold)
Electromagnetic Barrel Module
Hadronic EndCap
(2 wheels)
to interaction
vertex
p from LHC
Electromagnetic
EndCap
FCal1
FCal3
Cu Shielding
FCal2
total ~200,000 channels, with hadronic coverage ~10 absorption lengths in full acceptance (|η|<5) and a typical level of non-compensation e/h≈ ;

15. Calorimeter Signals: Towers
Imposes regular grid view on event
Δη×Δφ = 0.1×0.1
Motivated by event ET flow
Natural for trigger!
Calorimeter cell signals are summed up in tower bins
No cell selection, all cells are included
Indiscriminatory signal sum includes cells without any true signal at all
Sum typically includes geometrical weight
Towers have fixed direction
Massless four-momentum representation
projective cells
non-projective
cells

16. Calorimeter Signals: Topological Clusters
Pile-up Noise in Calorimeter Cells
S. Menke, ATLAS Physics Workshop 07/2005
Electronic Noise in Calorimeter Cells
Calorimeter Signals: Topological Clusters
Attempt to reconstruct particle showers
Establish local signal correlations in (neighbouring) cells
“energy blob” in 3-d
Growing volume algorithm using seeds and signal thresholds
Primary seeds start cluster
Secondary seeds among neighbours control growth
Basic cell selection threshold suppresses noise
All thresholds use signal-over-noise rather than signal
Signal significance is above a constant (but complex) threshold
Note: smallest reliably measurable energy is changing with noise!
Avoids regional energy thresholds (lots of tuning)
Uses experimentally accessible information
Gets the best out of the calorimeter!

17. Principle of Topological Clustering1 1 1
Primary seeds 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 4 1 4 1
Secondary seeds 1 5 1 1 5 1 5 1 1 1 1 4
1 5 4 4 1 4 1 4 1 2 1 5 2 1 5 1 5 1 4 5 4 1 1 4 4 1 1 4 2 2 2 5 2 1 5 2 1 2 2 2 2 2 2 2 3 3 3
Basic threshold 2 2 2 2 2 2 2 3 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3
Cluster splitting introduces geometrical weights!

18. Calorimeter Signals: It’s All In The Pictures…

19. Tower Jets in ATLAS
Sum up electromagnetic scale calorimeter cell signals into towers
Fixed grid of Δη x Δφ = 0.1 x 0.1
Non-discriminatory, no cell suppression
Works well with pointing readout geometries
Larger cells split their signal between towers according to the overlap area fraction
Tower noise suppression
Some towers have net negative signals
Apply “nearest neighbour tower recombination”
Combine negative signal tower(s) with nearby positive signal towers until sum of signals > 0
Remove towers with no nearby neighbours
Towers are “massless” pseudo-particles
Find jets
Note: towers have signal on electromagnetic energy scale
Calibrate jets
Retrieve calorimeter cell signals in jet
Apply signal weighting functions to these signals
Recalculate jet kinematics using these cell signals
Note: there are cells with negative signals!
Apply final corrections

20. Determination of Tower Jet Calibration
Sample of fully simulated QCD di-jet events from hard scatter pT>17 GeV/c to kinematic limit
Electronic noise included in simulation
Match reconstructed calorimeter jet with close-by particle jet
Both jets reconstructed with seeded cone R=0.7
pTseed>1 GeV/c
Overlap threshold 50%
Match exclusive: only accepted if only one jet close by
Calorimeter jets are based on tower signals in a grid of ΔηxΔη = 0.1x0.1
Access cell signals in jet
H1 motivated cell signal weighting strategy
Determine cell signal weights in resolution optimization fit using truth particle jet energy as normalization
Weights are function of cell location and cell signal density
Dense signals – em, less dense signals hadronic
Re-calculate jet four-momentum using cell weights
Jet energy and direction change

21. Tower Jet Performance Signal linearity Energy resolution
Relative to matched MC truth jet!
Energy resolution
Relative to truth
Kristin Lohwasser, September 2007
S. Padhi, ATLAS Physics Workshop 07/2005
S. Padhi, ATLAS Physics Workshop 07/2005

22. Tower Jet Uniformity Signal linearity as function of jet direction
Cracks less visible for wider jets, as expected
No strong jet energy dependence
Relative energy resolution as function of jet direction
Cracks deteriorate signal
Relative effect stronger dependend on jet energy, less on jet size
Kristin Lohwasser, September 2007
Kristin Lohwasser, September 2007

23. Cluster Jets in ATLAS Attempt to factorize
Noise suppression
Noisy cells are removed
Hadronic calibration
Signal weighting in cluster context, no jet bias
Dead material corrections
Limited to vicinity of clusters
Cannot correct if no signal at all nearby
Out-of cluster corrections
Efficiency correction for clustering algorithm
Provides calibrated input to jet finding
Relative mis-calibration O(5%)
Instead of O(30%)
Clusters can be interpreted as massless pseudo-particles
ATLAS convention, see later!
Cluster Jets in ATLAS

24. Why Cluster Jets At All? Reduce noise contribution
Fixed cone tower jet
Fixed cone cluster jet
Iacopo Vivarelli, September 2006
Iacopo Vivarelli, September 2006

25. Determination of cluster calibration
Classification
Use measurable cluster variables to determine if cluster looks electromagnetic, hadronic, or noisy
Cluster location (“early”) and average cell signal density useful to classify electromagnetic and hadronic clusters
Noisy clusters are typically seeded by negative signal
Often negative total cluster energy
Hadronic weighting
Apply only to hadronic clusters
Uses cluster direction, cluster energy, cell signal density and cell location (sampling layer) to find cell signal weights
Dead material corrections
Apply to hadronic and electromagnetic clusters
Uses DM corrections parametrized as function of cluster shapes
Out of cluster corrections
Account for lost true signal due to clustering
All corrections are derived from single pions and photons
Detailed simulations are needed for most of them
Can all be benchmarked with test beam data!
Cannot correct for everything at cluster level
No signal, no correction!

26. Cluster Jet Signal Linearity
Flat response in Et and rapidity
Forward region problems under study
Missing ~8% jet energy
~3% cluster mis-classification
Cluster classified as em, but really is hadronic
~3% signal efficiency
Signal of low energetic particles below cluster threshold
~2% electromagnetic calibration
Em clusters need own calibration
Basic scale insufficient
Remember: calibration so far comes from single particle!
No jet context whatsoever!
Studies under way…
We are now looking at topology based on clusters
Also started to look at jets
S.Menke/G. Pospelov
March 2007 T&P
S.Menke/G. Pospelov
March 2007 T&P

27. Jet Energy Scale Corrections
Use of kinematic constraints in pp
Photon/Z+jet pT balance
Central value model dependent!
Topology
Hard cuts on back-to-back no problem at LHC!
Kinematic limit ~400 GeV/c pT(photon) for 1%
First shot at jet energy scale!
W mass
Powerful but very special jets
W color-disconnected
Narrow jets
Di-jet balance
Extrapolation tool to high pT
Also detector uniformity
Topology dependence
Normalization strategy
Match reconstructed pT balance with particle level balance
Unfold topology dependences
Sigrid Jorgensen, September 2006

28. Doug Schouten, ATL-COM-PHYS-2007-057,September 2007
Photon+Jet
Missing Et Projection Fraction (MPF)
Pioneered by DØ
Low sensitivity to pile-up
No jet context needed
Can use clusters, towers, even cell signals
Doug Schouten, ATL-COM-PHYS ,September 2007
uncalibrated clusters

29. Jet Energy Scale From W Challenge: pile-up and W boost
Pile-up can “improve” jet energy scale!
W colour-disconnected from rest of event
Cannot expect the same particle flow around jet
Not straight forward to carry over corrections based on W mass to other jets at 1% level
P. Savard, P. Loch, CALOR97
η(W) ~1.8

30. Very High pT Jets Reconstruction concern: leakage
Find indicators in jet signal to tag leakage
Late showering in calorimeter
Use muon spectrometer hits behind jet
No energy measurement, but good tag
Studies underway to validate jet energy scale at very high pT
pT balance in systems with very high pT jet balancing several lower energetic jets
Frank Paige, ATLAS T&P Week February 2006

31. Jet Finder Efficiencies in ATLAS
Efficiency
Only free parameter: matching radius Rm
No kinematics matching!
Purity
Relates to fake rate
Jets in VBF!
Martin Schmitz, September 2007
Jets in VBF!
Martin Schmitz, September 2007

32. Pile-Up in Clone Cluster Jets
Expect cluster to suppress noise
Works for pile-up as well
Flucutations can be suppressed if correct noise RMS used in cluster finder
Cluster noise cuts are symmetric!
Some energy offset observed
Pile-up is asymmetric
Baseline larger for correct RMS
Bias toward positive signals by noise selection
Doug Schouten, ATL-COM-PHYS ,September 2007
Et Pile-up in R=0.2 cone
Doug Schouten, ATL-COM-PHYS ,September 2007

33. PL & Chiara Paleari, Poster @ SLAC ATLAS Workshop, August 2007
relative mass difference
cluster jets
tower jets
PL & Chiara Paleari, SLAC ATLAS Workshop, August 2007
Jet Masses
Gained interest at LHC
Heavily boosted top decays
All decay products reconstructed in one jet
Jet mass one observable indicating top decay
Jet substructure also sensitive to source!
Mass measurement challenging
Particle jet level mass is reference
Simulations only!
Mass of calorimeter jet is affected by shower spreads
Enters: signal definition dependence, cluster shapes, noise,…
No significant attempt at Tevatron or elsewhere

34. Mass Reconstruction Sensitivities
Contribution from low energetic particles lost
Dead material and magnetic field
Overall effect depends on signal definition
How about effect on mass?
Exercise: remove particles below pT threshold from jet and re-calculate mass
Remember: towers are not calibrated
More severe effect of cut in tower jets
Clusters are calibrated
More similar to particle selection in jets

35. Mass Sensitivity change of mass QCD kT jets, D = 0.6
log10(least biased reconstructed mass/GeV)
QCD kT jets, D = 0.6
Mass Sensitivity

36. J. Butterworth et. al,, ATL-COM-PHYS-2007-077,October 2007
Jet Substructure
Mass too complex?
Can be too sensitive to small signals in jets
UE, pile-up, other noise
Use YSplitter to detect substructure
Determines scale y for splitting a giving jet into 2,3,… subjects, as determined by ycut, from
More stable as only significant constituents are used ?
At least additional information to mass
Other option:
Look at mass of 2…n hardest constituents (Ben Lillie,ANL)
J. Butterworth et. al,, ATL-COM-PHYS ,October 2007
Not very
sensitive to
calorimeter
signal details!

37. Jet Shapes (1)
1st question: any relation between number of particles, towers, clusters in jets?
Most interesting for kT
D = 0.6 here
Look at matching callorimeter/truth jets
Note: not the most important variable!
We already expect change of “jet picture” by detector signal definition
Hints on resolution power for jet shape variables and mass

38. Jet Shapes (2) We expected clusters to represent indivdual particles
Cannot be perfect in busy jet environment!
Shower overlap in finite calorimeter granularity
Some resolution power, though
Much better than for tower jets!
~1.6:1 particles:clusters in central region
~1:1 in endcap region
Best match of readout granularity, shower size and jet particle energy flow
Happy coincidence, not a design feature of the ATLAS calorimeter!

39. PL & Chiara Paleari, Poster @ SLAC ATLAS Workshop, August 2007
cluster jets
tower jets
hadron jets
Fraction of energy outside cone around jet axis (Rcone=0.3)
log10(pTjet/GeV)
PL & Chiara Paleari, SLAC ATLAS Workshop, August 2007
Jet Shapes (3)
Jet density
Calculate energy outside of cone R = 0.3 as function of pT and direction
Classic Tevatron measurement
Experimental indication of transition from (low pT) gluon to (high pT) quark jets
Example: kT jets in QCD
D = 0.6

40. Summary General Jet signals What we can get from jets
Everything you have seen here from ATLAS is based on simulations and thus very preliminary
Real data can bring us surprises (good and bad)
Jet signals
Cluster signal (~200/event) good basis for jet finding in physics analysis context
Final jet energy scale corrections depend on analysis choices for jet finders, -configurations, selected event topologies…
What we can get from jets
Strong interest to go beyond Tevatron jets
Jet masses and substructure analysis of great interest for boosted heavy particle decays
Jet shapes can test basic jet formation dynamics ??
New dimension added: jet signal shapes in calorimeters can improve calibration jet by jet

41. Outlook Refined jet calibration Jet origins
Use calorimeter jet signal shapes
We used cluster shapes already, now look at cluster distribution in jet
Use inner detector tracks
Large momentum fraction of jet in tracks indicates a very hadronic jet, i.e. more corrections
Jet origins
Use inner detector tracks and vertices to separate jets from hard scattering from jets from UE and/or pile-up (A. Schwartzman)
All this has not been used much in the past, but we have to address a 1% systematic error requirement somehow!
We are more than ready for data!

42. Backup Slides

43. Jet Energy Scale (JES) JES error very quickly systematically dominated
Large statistics in unexplored kinematic range already at low luminosity
Calibration channels quickly accessible
pT balance
Photon + jet(s)
Z+jet(s) later
Mass spectroscopy
W->jj in ttbar
Dominant direct photon production gives access to gluon structure at high x
(~ ) (precision ?)

44. Measurements with Jets
just from cross-section, can be improved by 3/2 jet ratio, but no competition for LEP/HERA!
Strong Coupling
test of QCD at very small scale ( )
PDFs
di-jet cross section and properties (Et,η1,η2) constrain parton distribution function
Compositeness
Deviation from SM
sensitivity to compositeness scale Λ up to fb-1 (all quarks are composites)

45. “H1” Style Cell Signal Weighting in ATLAS
Fit constraint:
Jet four-momentum calculation after fit
Final corrections for residual signal non-linearities
Algorithm dependencies
Available for seeded cone R=0.4, kT D=0.4, D=0.6
Signal dependencies (cluster/tower)
“massless pseudo-particles”