Taus and Jets

LHC PhysicsGRS PY 898 B8

Lecture #7

Tulika Bose

March 2nd, 2009

Taus and Jets

2

• 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

3

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

4

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

5

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

6

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

7

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

8

• 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

10

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

12

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

13

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

• Neural Net performance

ATLAS Tau1P3P: Performance

15

CMS: Ecal isolation

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

16

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

17

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

18

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

19

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)

20

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

21

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– 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



• Collinear safety– Particles split into two with

same total energy should not change results

– Minimize sensitivity due to ET ordering of seeds



• 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

Algorithm Flow ChartsClustering Algorithm Merging/Splitting Algorithm

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

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

€

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

Fast KT

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 Finding Efficiency

Response

€

Response = Calorimeter jet pT

particle jet pT

Jet calibration strategy

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

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 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

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

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

Flavor Correction

Multiple InteractionsData-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 ->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

Jet Resolution

Energy vs Momentum Resolution

Can use tracking information to improve jet resolution

33.03.04

7202

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

Offset correction

Jet reconstruction: noise (incoherent) in ATLAS

Jet reconstruction: noise (coherent) in ATLAS

Taus and Jets

Documents

Transcript of Taus and Jets