1. Experimental Aspects of Jet Reconstruction (Lecture 2)
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 jet features at LHC
Focus QCD!
Jet reconstruction in ATLAS
Strategy I calibration implementation (jet context based)
Tower and cluster jets
In-situ calibration
Forward jets
More from jets at LHC
Mass reconstruction
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 no be achieved for all kinds of jets and in all physics environments

4. Inclusive Jet Cross-section at LHC
Changing jet flavours in QCD
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” generated in pp collisions at LHC
→ expect corresponding variety of jet shapes with (possibly) specific calibrations!

5. QCD Jet Multiplicities @ LHC
Jet multiplicities in 2→2 scattering
Di-jet mass range

6. Jet Production @ LHC Expectations:
Jet energy scale error very quickly systematically dominated
Large statistics in unexplored kinematic range already at low luminosity
Calibration channels quickly accessible
Especially for photon+jet(s)
Dominant direct photon production gives access to gluon structure at high x
(~ )

7. QCD Measurements at LHC
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)

8. Physics Environments @ LHC
Physics environment different from Tevatron
Increased underlying event activity (more phase space)
Already at lowest (initial) luminosities ~ cm-2 s-1
Additional activity from pile-up
Proportional to instantaneous luminosity
A.Moraes, HERA-LHC Workshop,
DESY, March 2007
LHC prediction: x2.5 the activity measured at Tevatron!
Number charged tracks in transverse region
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)

9. Physics Environment @ LHC (2)
Section: Jets at LHC
Et ~ 58 GeV
Et ~ 81 GeV
no pile-up added
LHC design luminosity pile-up added
Physics LHC (2)
Additional challenges: Pile-up
High bunch crossing rate at LHC (40 MHz) and large pp cross-section (~75 mb)
About 23 (soft) collisions in addition to the triggered hard scattering at design luminosity 1034 cm-2 s-1
Poisson distributed
Statistically independent
Low pT scattering mostly (“minimum bias”)
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
Similar dynamics but no correlation with hard scattering
Calorimeter signals typically to slow
Short signal shaping, bi-polar shaping function (ATLAS)
Long signal history (~ ns),
Out of time pile-up
Keep as much signal as possible
High magnetic field (4 T) and high signal threshold (CMS)
Suppress small signal contribution
P. Savard et al., ATLAS-CAL-NO 084/1996
R = 0.7

10. Physics Environments @ LHC (3)
Discovery physics at LHC
Expect extreme busy final states
Lots of leptons, missing Et and jets O(10) in SUSY
Many x 10 jets in black hole production
Good spatial resolution power to find the jets
Good energy resolution for reliable missing Et calculation
Need large rapidity coverage
Tag vector boson fusion production of Higgs and exotics
WW, WZ, ZZ with associated quark jets
These “tag jets” often go forward
Jets are uncorrelated with each other, but balance the central system (Higgs)
Tagjet direction η
100 GeV
600 GeV
300 GeV
Direction of tag jets in Higgs VBF production for mH = 100, 300, 600 GeV
this is a very old plot!

11. Jet Finders in ATLAS Defaults are seeded cone and kT
Also implementations of 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
Narrow jets
W->jj in ttbar, some SUSY
Wider jets
Inclusive jet cross-section, QCD mW
N.Godbhane, JetRec June 2006
Algorithm
Cone Size R
Distance D
Clients
Seeded Cone
EtSeed = 1 GeV
fS/M = 0.5
0.4
W mass spectroscopy, top physics
Kt (FastKt)
0.7
QCD, jet cross-sections
0.6
P.-A. Delsart, June 2006

12. 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

13. 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, see lecture 1
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

14. “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”

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

16. ATLAS Tower Jet Features
Algorithm and physics environment
Remember: no universal calibration
Fa,s recovers some signal linearity
cannot redo cell weight fits for all possible scenarios
Calorimeter signal choice
Loss of signal if taking clusters instead of towers
Noise cut!
Width ~unchanged
Weights still work
old but educational!
F. Paige, ATLAS Software Workshop 09/2004
jets/0.02
Reconstructed Et/True Et
tower jets
cluster jets
F. Paige, ATLAS Jet/EtMiss Working Meeting 02/02/2005

17. Input Selection in Tower kT Jets
Early attempt to deal with “vacuum” effect in kT
Remove low energetic particles and signals from final state before jet finding by ET cut
Problematic: ET uncalibrated for towers → cut effectively higher (~30%) for calorimeter jets!
jet transverse energy
ok!
input bias
very good agreement!!

18. Effect on Tower Jet Shapes
Experimentally accessible
Need to look at it again with data
Again biggest problem uncalibrated constituents of jets
We could feed jet calibration back to tower signals
Distance from jet axis
Distance from jet axis
em scale!

19. 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

20. It’s All In The Pictures…

21. Why Cluster Jets At All? Reduce noise contribution
Fixed cone tower jet
Fixed cone cluster jet

22. 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…
S.Menke/G. Pospelov
March 2007 T&P
S.Menke/G. Pospelov
March 2007 T&P

23. 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

24. Photon+Jet Resolution bias Choice of selection variables
Average photon+jet pT
Problematic in jet pT correction
Change of event selection??
pT projection as function of pT(photon) more appropriate?
photon Pt cut
average Pt cut (pTγ+pTparton)/2

25. Jet Energy Scale From W Challenge: pile-up and W boost
Pile-up can “improve” jet energy scale!
P. Savard, P. Loch, CALOR97
η(W) ~1.8

26. 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
Frank Paige, ATLAS T&P Week February 2006

27. Jet Finder Efficiencies in ATLAS
Efficiency
Only free parameter: matching radius Rm
No kinematics matching!
Purity
Relates to fake rate
Jets in VBF!
Jets in VBF!

28. Forward Jets Pile-up Noise in Calorimeter Cells Complex region
S. Menke, ATLAS Physics Workshop 07/2005
Forward Jets
Complex region
Low pt jets
Especially in VBF!
Same fluctuations (in Et) from pile-up as in central region!
Signal significance a concern
What do we need from it
High tagging efficiency
Need to find VBF
Kinematics reconstruction
Limited by fluctuations
Limits “invisible Higgs” reconstruction
e/jet separation
Forward/backward asymmetry in Z decays
Access to sinθw beyond LEP precision due to large rapidity coverage
very detector dependent performance!
ATLAS only here!
very old!
jet energy fluctuations
jet cone size

29. Forward Jets: Kinematic Reconstruction
In presence of pile-up
Design luminosity
covered by endcap
RMS Pile-Up in R=1.0 Jet 1034 s-1cm-2
RMS Pile-Up in R=0.4 Jet 1034 s-1cm-2
Jet Cone Size R
Signal Significance
1034 cm-2s-1
P.Loch & P.Savard, CALOR97
Forward tag jets in Higgs production have average Et of GeV only (descreasing with increasing η)
acceptance limit
ATLAS Physics and Detector Performance TDR, Vol 1.,
LHCC-99-14/15, Fig.9-24

30. Jet Masses Gained interest at LHC Mass measurement challenging
Tower Jet Mass
Jet Masses
Gained interest at LHC
Heavily boosted top decays
All decay products reconstructed in one jet
Jet mass only observable indicating top decay
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
So how about LHC
Really ATLAS for this lecture!
Truth Jet Mass
Cluster Jet Mass
Truth Jet Mass
Tower Jet Mass
Truth Jet Mass
Cluster Jet Mass
Truth Jet Mass

31. Jet Mass Reconstruction
cluster jets
tower jets
Signal definition dependence
Expected from four-vector recombination in jet finding
E.g. tower jets have about constant number of constituents in cone jets:
Different noise contribution in clusters and towers
Tower split shower signals
Energy sharing between towers depends on shower size wrt readout granularity and internal Et flow in jet → direction dependence
Cluster collects shower signal
Non-resolvable shower overlap limits mass resolution → direction dependence
cluster jets
tower jets
cluster jets
tower jets
relative mass difference

32. Mass Reconstruction Sensitivities
Contribution from low energetic particles lost
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

33. Mass Sensitivity (2) change of mass QCD kT jets, D = 0.6
log10(least biased reconstructed mass/GeV)

34. 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

35. 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!

36. Jet Shapes (3) Jet density Example: kT jets in QCD
cluster jets
Jet Shapes (3)
tower jets
hadron jets
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
Fraction of energy outside cone around jet axis (Rcone=0.3)
log10(pTjet/GeV)

37. Jet Shapes (4) Integrated Et flow in jets
Average projection of constituent pT vector on jet pT vector as function of distance from jet axis
Differential spectrum
Integrate up to R
Can be done with real data!
Perpendicular component indication of jet dynamics ?
Jet by jet?

38. Jet Shapes (5)

39. Summary General Jet calibration What we can get from jets
Everything you have seen here from ATLAS is based on simulations
Real data can bring us surprises (good and bad)
Jet calibration
Local calibration based on clusters seems to be the way to go for a first order hadronic calibration
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 of great interest
Jet shapes can test basic jet formation dynamics ??
New dimension added: jet signal shapes in calorimeters

40. Lots is left to do… Refined jet calibration
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
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!