Taus and jets
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LHC Physics GRS PY 898 B8 Lecture #7 Tulika Bose March 2nd, 2009. Taus and Jets. Taus @ LHC. Standard model processes Z -> tt, W -> tn Useful for validation, tuning of the algorithms, calibration Searches Many discovery channels involve tau leptons in the final state:

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LHC PhysicsGRS PY 898 B8Lecture #7

Tulika Bose

March 2nd, 2009

Taus and Jets


Taus @ LHC

  • Standard model processes

    • Z -> tt, W -> tn

      • 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 -> tt, H+-> tn

      • SM Higgs

        • qqH->qqtt, ttH->tttt


Tau characteristics

  • ct ~= 87mm, mt=1.78 Gev/c2

  • Leptonic decays

    • t-> e(m) n n : ~ 35.2 %

      • Identification done through the final lepton

  • Hadronic decays (~65%)

    • 1 prong

      • t -> nt + p+/- + n(po) : 49.5 %

    • 3 prongs

      • t -> nt + 3p+/- + n(po) : 15.2 %

    • “t-jet” is produced

Tau decay branching ratios


Tau characteristics

  • 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 po

Often taus are produced in pairs: 42% of final states contain two “tau-jet”


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


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


ATLAS: tauRec

  • Calorimeter-based tau reconstruction

  • Start from >15 GeV Cluster in region Δη X Δφ = 0.1 X 0.1

  • Tau candidate reconstruction

    • Collimated Calorimeter cluster

    • small number of associated charged tracks

  • Preliminary requirements:

    • abs(h)<2.5

    • Collimated Calorimeter Clusters (ET>15 GeV)

    • One or three associated “good” charged tracks

      • PT>2GeV

      • DR(track,cluster center)<0.3


ATLAS: tauRec

8

Iso fraction

Calorimeter Isolation

j runs over all cells within 0.1 <ΔR < 0.2

i runs over all cells within ΔR < 0.4

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

Though the likelihood is formed in bins of ET, it still shows some ET dependence. Cuts used in analysis should therefore depend on ET.

88.5 < pT < 133.5

likelihood

  • Likelihood built with the previously described variables

  • Cut on likelihood depend on ET

pT < 28.5 GeV

Neural Network and cut-based

approaches are also supported.


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, DR<0.2

    • DR(track-direction,jet-direction)<0.2

    • Require isolation in ring 0.2< DR<0.4

    • Calculate several discriminant variables like in TauRec case


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


ATLAS Tau1P3P: Performance

  • Neural Net performance


CMS: Ecal isolation

Signal efficiency of about 80% with a bkg rejection of ~5 for QCD jets with pT>80 GeV


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

  • pTLT : 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


Single Tau

(30<ET<150 GeV)

QCD jets

50<ET<170 GeV

CMS: tau jets and QCD jets efficiency

In the order of decreasing efficiency

symbols correspond to decreasing MC ET intervals

Cuts used:

8 hits per track, Norm. Chi2 < 10

PtLT > 6 GeV/c, RM = 0.1, RI = 0.2-0.5, PTI > 1 GeV, |Dz| < 2mm


CMS: other tagging methods

  • 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

I.P. distribution

Tau

1 prong

QCD

1 track

Transverse I.P. efficiency


CMS: tagging with decay length

  • The lifetime of the tau lepton (ct = 87 mm) 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

Fake vertices in the pixel

material removed by

cutting on the transverse

flight distance (< 4 cm)

3D decay length

  • Both “Transverse” decay length and 3D decay length considered


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 p0)

CMS

  • Use particle flow (PF)

    • Essentially reconstruction and identification of every constituent particle inside the tau

      • Electrons, photons, p±, po, neutral hadrons


Jets


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

    • Wjj 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


Algorithm Considerations

Initial 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 Wjj 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 ETacts 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


Algorithm Flow Charts

Clustering Algorithm

Merging/Splitting Algorithm


Problems with Cone-based Clustering Algorithms

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

The addition of a threshold to the seed towers makes the algorithm “collinear unsafe”


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


SISCone


SISCone


SISCone

SISCone is 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


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….


Fast KT


Performance of Different Jet Algorithms

JetClu

Midpoint

Used by CDF

Different jet algorithms find different jets

kT Clustering

Midpoint leaves unclustered towers

Dark Towers

kT has jets with poorly defined

boundaries


Jets


Jet Finding Efficiency


Response


Response


Jet calibration strategy


Jet Corrections

Correct Jet Energy to the particle level

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


Jet Corrections


Underlying Event


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 h for a fixed jet pT

  • h dependent corrections make the response flat as a function of h

  • 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 h

  • 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


Absolute Correction (data)


Absolute Correction (data)


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

DpT = 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

    • => g/Z+jets samples will have higher response than QCD dijet

    • Corrections can be obtained from MC truth and ttbar data

QCD dijet


Flavor Correction


Multiple Interactions

Data-based correction


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 p->gg events for uniform azimuthal response

      • Use Z->ee and W->en events for absolute EM scale

    • HCAL calibration

      • Use zero-bias and minimum bias events to equalize tower-to-tower response in azimuth (f); 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 h


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 corrections

applied to the calorimeter jets

Jet energy scale will be a dominant experimental systematic uncertainty for dijet resonance searches

Even a small amount of data (<100pb^-1 at 10 TeV) would be sufficient to surpass the Tevatron sensitivity


Jet Resolution


Energy vs Momentum Resolution

Can use tracking information to improve jet 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

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


Offset correction


Jet reconstruction: noise (incoherent) in ATLAS


Jet reconstruction: noise (coherent) in ATLAS


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