Post on 20-Dec-2015
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• Standard model processes– Z -> W ->
• Useful for validation, tuning of the algorithms, calibration
• Searches– Many discovery channels
involve tau leptons in the final state:
• MSSM Higgs (A/H, H+)
– A/H -> H+-> • SM Higgs
– qqH->qq, ttH->tt
Taus @ LHC
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Tau characteristics• cm, mGev/c2
• Leptonic decays– e() ~ 35.2 %
• Identification done through the final lepton
• Hadronic decays (~65%)– 1 prong
• n
– prongs
• 3n
– “jet” is produced Tau decay branching ratios
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Tau-jets at LHC:Tau-jets at LHC:• Very collimated
•90% of the energy is contained in a ‘cone’ of radius R=0.2 around the jet direction for ET>50 GeV
• Low charged track multiplicity•One, three prongs
• Hadronic, EM energy deposition•Charged pions•Photons from o
Often taus are produced in pairs: 42% of final states contain two “tau-jet”
Tau characteristics
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Tau identification
• tau tagging basic ingredients– Calorimetric isolation and shape variables– Charged tracks isolation– Other tau characteristics suitable for tagging:
• Impact parameter• Decay length• Invariant Mass
• Main backgrounds for taus– QCD jets– Electron that shower late or with strong bremstrahlung– Muons interacting in the calorimeter
• Only hadronic tau decays are considered since the leptonic tau decays produce standard electrons/muons– Identification through the final lepton
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ATLAS tau algorithms
• TauRec– Calorimeter + inner detector are used– Energy from calorimeter is calibrated– Tracks+clusters are associated – Several suitable variables are computed
• Likelihood or variable cut used for tau-id• Tau1P3P
– Intended for studies of low mass Higgs• Visible tau hadronic energy 20-70 GeV
– Identify a leading track• Single prong (Tau1P) or three-prong (Tau3P) candidate
– Compute set of discriminating variables• Apply set of basic cut for tau-id or use Multi-variate
classification technique
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ATLAS: tauRec
• Tau candidate reconstruction– Collimated Calorimeter cluster– small number of associated charged
tracks
Calorimeter-based tau reconstruction• Start from >15 GeV Cluster in region Δη X Δφ = 0.1 X 0.1
• Preliminary requirements:– abs()<2.5– Collimated Calorimeter Clusters (ET>15
GeV)– One or three associated “good” charged
tracks• PT>2GeV• R(track,cluster center)<0.3
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• Calorimeter Isolation
• j runs over all cells within 0.1 <ΔR < 0.2• i runs over all cells within ΔR < 0.4
8Iso fraction
• EM-radius
• i runs over all cells in cluster (n total cells)
ATLAS: tauRec
• Track multiplicity• pT > 2 GeV
• Powerful for high ET discrimination between jets and taus
• Tau charge• Sum of charges of all
associated tracks
• Transverse energy width in η strip layer
ATLAS: tauRec
ATLAS:tauRec performance
likelihood
Though the likelihood is formed in bins of ET, it still shows some ET dependence. Cuts used in analysis should therefore depend on ET.
Neural Network and cut-basedapproaches are also supported.
pT < 28.5 GeV
88.5 < pT < 133.5
•Likelihood built with the previously described variables•Cut on likelihood depend on ET
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ATLAS: Tau1P3P• Track-based reconstruction
– Start with a seed track with pT > 9 GeV– Create candidates with up to 5 tracks
(3 quality tracks)
• Tau candidates preselection– “Good” hadronic leading track PT>9
GeV– 0 or 2 nearby-track: PT>2GeV, R<0.2– R(track-direction,jet-direction)<0.2– Require isolation in ring 0.2< R<0.4– Calculate several discriminant variables
like in TauRec case
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ATLAS Tau1P3P: multivariate analysis
• Signal and background samples are used to combine the observables to a single one, called “discriminant”
• It is possible to cut on discriminant to separate signal from background • Better background rejection is obtained with the same signal efficiency
Tau1P results
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CMS: Ecal isolation
Signal efficiency of about 80% with a bkg rejection of ~5 for QCD jets with pT>80 GeV
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CMS: Tracker isolation
• Isolation based on the number of tracks inside the isolation cone (RI) is applied.
• Only good tracks are considered:• Associated to the Primary Vertex• PT of the Leading Track (i.e. highest pT
track) must exceed a few GeV/c• Leading Track must be found inside the
Matching cone• RM : calo jet - leading track matching cone
• pT
LT : cut on pT of the leading track in matching cone
• RS : signal cone around leading track
• RI : isolation cone (around jet axis or leading track)
• pTi : cut on pT of tracks in the isolation cone
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CMS: tau jets and QCD jets efficiency
Cuts used:8 hits per track, Norm. Chi2 < 10Pt
LT > 6 GeV/c, RM = 0.1, RI = 0.2-0.5, PTI > 1 GeV, |Dz| < 2mm
Single Tau (30<ET<150 GeV)
QCD jets50<ET<170 GeV
In the order of decreasing efficiency symbols correspond to decreasing MC ET intervals
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Tracker isolation is a primary requirement for the tau jet identification
All the following tagging methods are applied to jets which preliminarly pass the Tracker Isolation:•Tagging with IP•Tagging with decay length•Mass tag
CMS: other tagging methods
Tau1 prong
QCD1 track
Transverse I.P. efficiency
I.P. distribution
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CMS: tagging with decay length• The lifetime of the tau lepton (c =
87 m) allows for the reconstruction of the secondary vertex for the 3 (and 5) prongs decay
• Events are required to pass tracker isolation, only tracks inside signal cone are used in the vertex fitter
• Both “Transverse” decay length and 3D decay length considered
3D decay length
Fake vertices in the pixelmaterial removed bycutting on the transverseflight distance (< 4 cm)
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Tau Jet energy scale
• Both Atlas and CMS apply energy scale corrections
• Tau jets need softer corrections to their energy, wrt QCD jets.• At the same transverse energy,
pions in Tau jets have harder transverse momentum than pions in QCD jets
• In Tau jets there is a larger amount of electromagnetic energy (due to the presence of 0)
CMS
• Use particle flow (PF)•Essentially reconstruction and identification of every constituent particle inside the tau
•Electrons, photons, ±, o, neutral hadrons
Jets @ LHC• Gluon jets from parton scattering
– Mostly in lower pt QCD processes• Quark jets from parton scattering
– Higher pT QCD processes– Dominant prompt photon
channel, Z+jet…– Final state in extra dimensions
models with graviton force mediator
• Quark jets from decays– Wjj in ttbar decays– End of long decay chains in SUSY
and other exotic particle production
• Jets needed for both precision and discovery physics in wide range of energies
Jet Finding
• After the hard interaction, partons hadronize into stable particles
• Stable particles interact with the detectors
• Jet algorithms cluster energy deposits in the EM and HAD calorimeter, or more generally four-vectors
• Jet algorithm provides a good correspondence between what is observed and the hard interaction
Jet Finding
• Particle Jet– a spread of particles
running roughly in the same direction as the parton after hadronization
Jet Finding
• Calorimeter Jet– jet is a collection of energy
deposits with a cone R: – Cone direction maximizes the
total ET of the jet– Various clustering algorithms€
R = Δϕ 2 + Δη 2
Algorithm ConsiderationsInitial considerations• Jets define the hadronic final state of basically all physics channels
– Jet reconstruction essential for signal and background definition– Applied algorithms not necessarily universal for all physics scenarios
• Mass spectroscopy Wjj in ttbar needs narrow jets– Generally narrow jets preferred in busy final states like SUSY– Increased resolution power for final state composition
• QCD jet cross section measurement prefers wider jets– Important to capture all energy from the scattered parton
• Which jet algorithms to use ?– Use theoretical and experimental guidelines collected by the Run II
Tevatron Jet Physics Working Group• J. Blazey et al., hep-ex/0005012v2
Theoretical Considerations• Infrared safety
– Soft particles in between jets should not change jets
Artifical split due to absence of gluon radiation between two partons/particles
Theoretical Considerations• Infrared safety
– Soft particles in between jets should not change jets
• Collinear safety– Particles split into two with
same total energy should not change results
– Minimize sensitivity due to ET ordering of seeds
Theoretical Considerations• Infrared safety
– Soft particles in between jets should not change jets
• Collinear safety– Particles split into two with
same total energy should not change results
– Minimize sensitivity due to ET ordering of seeds
• Invariance under boost– Same jets in lab frame of
reference as in collision frame• Order independence
– Same jet from partons, particles, detector signals
Experimental Considerations• Detector technology independence
– Jet efficiency should not depend on detector technology• Final jet calibration and corrections ideally unfold all detector effects
• Stability within environment– (Electronic) detector noise should not affect jet reconstruction within reasonable
limits• Energy resolution limitation• Avoid energy scale shift due to noise
– Stability with changing (instantaneous) luminosity• Control of underlying event and pile-up signal contribution
• “Easy” to calibrate– Small algorithm bias for jet signal
• High reconstruction efficiency– Identify all physically interesting jets from energetic partons
• Efficient use of computing resources– Balance physics requirements with available computing
Jet Algorithms: Cone• Cone-based algorithms have been traditionally used at hadron
colliders• Particles above certain pT threshold are seeds
– 1) for each seed, all particles within ΔR < R are added to the seed to form a new jet
– 2) if (η|ϕ) of the seed coincide with the jet axis (Δη|Δϕ < 0.05), jet is considered stable, otherwise new jet axis is used for the direction of the new seed and algorithm returns to point 1)
• If final jet shares more than a fraction f of energy of higher ET jet, jets are merged, otherwise shared particles are are assigned to the closest jet
Steps in a Clustering Algorithm
Start from a collection of four-vectors, highest ET acts as seed
Sum all four-vectors within cone to form a proto-jet
Repeat until proto-jet is stable (axis of proto-jet aligns with the sum of four-vectors within the cone) or reach maximum iterations
Repeat until all towers are used
Apply splitting/merging algorithm to ensure four-vectors are assigned to only one proto-jet
→ Left with list of all stable jets
Problems with Cone-based Clustering Algorithms
The addition of a threshold to the seed towers makes the algorithm “collinear unsafe”
Seed based cone algorithms are typically “infrared unsafe”
Addition of an an infinitismally soft particle can lead to new stable jet configurations → sensitive to hadronization model and order of perturbative QCD calculation
Problems arise when comparing to higher order p-QCD calculations
Variants of Cone jet finders
• Simplified algorithm (iterative cone) – Once stable jet is found remove its constituents from the input list
and start again…– Fast and therefore typically used in the trigger– Collinear and infrared unsafe
• MidPoint algorithm places additional seeds in between doublets (or triplets ) of initial seeds– Reduced infrared sensitivity; collinear unsafe
• Seedless cone works without any seeds but is very CPU intensive• SISCone
– fast seedless implementation is now available (collinear safe)– Infrared safe
Jet Algorithms: kT• Iterative procedure to cluster pairs of particles based on “closeness”• For each pair calculate:
• Find minimum of all dii, dij
– If dii, call it jet
– If dij, combine particle I and j
• Iterate until only jets left• Parameter D controls the termination of the merging and characterizes the size of the resulting jet
€
dii = kT ,i2
dij = min(kT ,i2 ,kT , j
2 )ΔRij
2
D2
Variants of kT
• Iterative and seedless procedure makes it infrared and collinear safe
• Different distance measures available– KT (p=1)– Aachen/Cambridge(p=0)– Inverse or anti-kT (p=-1)
• Different distance measures behave differently in terms of hadronisation corrections, pile-up
• However, slow to execute• Recently a faster version has been made available….
€
dij = min(kT ,i2p,kT , j
2p )ΔRij
2
D2
Performance of Different Jet Algorithms
Midpoint leaves unclustered towersDark Towers
Different jet algorithms find different jets
Midpoint
kT Clustering
JetClu
kT has jets with poorly defined boundaries
Used by CDF
Jet Corrections
With corrections (MCJet) derived entirely from the Monte Carlo simulation, CaloJets can be corrected to GenJets using “MC truth” information
Being replaced by the factorized corrections
Correct Jet Energy to the particle level
Offset correction• Pile-up of multiple proton-proton collisions and electronic noise in the
detector produce an energy offset• Offset correction subtracts, on average, the unwanted energy from the jet• Estimate using noise-only and minimum bias MC events
Offset correction• Calorimeter tower threshold ET > 0.5 GeV suppresses pile-up• Offset due to electronic noise is found to be negligible• Offset due to a single pile-up event is small: <ET>= 0.1-0.3 GeV
– Offset will increase with number of pile-up events• Estimated using zero-bias data; data triggered on in the presence of bunch
crossings but vetoing hard interactions; minimum-bias triggered events
Relative Response Correction
• Jet response varies as a function of jet for a fixed jet pT
• dependent corrections make the response flat as a function of
• Correction can be determined from MC truth and later on from QCD dijet events in collision data
Relative Response Correction
• Use pT balance in back-to-back dijet events
• One jet in the central control region (barrel jet)
• Other jet (probe jet) with arbitrary
• Use dedicated calibration triggers to collect dijet events
Absolute Correction
• Calorimeter energy response to a particle level jet is smaller than unity and varies as a function of jet pT
• pT dependent corrections remove these variations and make the response equal to unity
EMF Correction• Fraction of jet energy deposited in the EM Calorimeter (EMF)
provides additional information that can be used to improve the jet energy resolution
• Optional correction; can be used for analyses requiring optimal jet energy resolution
pT = GenJet pT – corr. CaloJet pT
Flavor Correction• Corrects a jet to the particle level assuming the jet originated
from a specific parton flavor (as opposed to the QCD dijet mixture of parton flavors used previously)– E.g. light quarks have higher response than gluons since they
fragment into higher momentum particles– => +jets samples will have higher response than QCD dijet– Corrections can be obtained from MC truth and ttbar data
QCD dijet
Parton Corrections• These take the jet back to the corresponding parton• GenJet response to an input parton depends on the parton flavor
– Gluons radiate more than light quarks have a lower GenJet response (since more final state radiation falls outside the jet)
• GenJet response depends on the size of the jet with the response increasing with cone size (or D)
• Can be determined from dijet MC events
Jet Energy Corrections at startup • Jet energy correction and its systematic uncertainty depend on
the underlying calibration of the ECAL and HCAL• Calorimeter workflow
– ECAL calibration• Use isolated ->events for uniform azimuthal response• Use Z->ee and W->e events for absolute EM scale
– HCAL calibration• Use zero-bias and minimum bias events to equalize tower-
to-tower response in azimuth (); separately for barrel, endcap and forward
• Use single isolated tracks to calibrate calorimeter towers in HB and HE with respect to the momentum measurement in the tracker
• Use dijet events to achieve tower response uniformity in
Jet Energy Corrections
Jet Energy Correction Worklow• Derive MC truth based jet energy corrections using realistic MC
simulation• Do not include jet energy corrections in the trigger level
– Apply a simple factor of 0.7 to HF towers to flatten the calorimeter response over eta
– Also take data for a few fills without any correction factors to study possible biases
• Extract ECAL and HCAL calibrations from data; reprocess data• Derive offset, eta-dependent, pT dependent corrections from
data using dijet pT balance• Reprocess data…
Corrected Dijet Mass
Reconstructed dijet mass distribution for Z’ events
GenJet: clustered stable particles,does not include detector effects, includes neutral particles
CaloJet: clustered calorimeter jets, uncorrected
Corrected CaloJet: jet correctionsapplied to the calorimeter jets
Jet energy scale will be a dominant experimental systematic uncertainty for dijet resonance searchesEven a small amount of data (<100pb^-1 at 10 TeV) would be sufficient to surpass the Tevatron sensitivity
Energy vs Momentum Resolution
Can use tracking information to improve jet resolution
33.03.04
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BL
p
npT
T
pT
Jet+Tracks (JPT) corrections are available (see WorkBook)
Calorimeter energy resolution becomes better as E increases while the tracker’s momentum resolution becomes worse
Exercise: Find momentum at which tracker resolution equals calorimeter resolution
For: n=100, B=1T, L=1m, σ=250μm
» 250 GeV/c we can’t reliably measure the charge of the particle at the 3σ level