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Taus and Jets

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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Taus and Jets

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  1. LHC PhysicsGRS PY 898 B8Lecture #7 Tulika Bose March 2nd, 2009 Taus and Jets

  2. 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

  3. 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

  4. 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”

  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 • 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

  8. 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)

  9. 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

  10. ATLAS: tauRec

  11. 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.

  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, 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

  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

  14. ATLAS Tau1P3P: Performance • Neural Net 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 • 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

  17. 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

  18. 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

  19. 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

  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 p0) CMS • Use particle flow (PF) • Essentially reconstruction and identification of every constituent particle inside the tau • Electrons, photons, p±, po, neutral hadrons

  21. Jets

  22. 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

  23. 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

  24. Jet Finding • Particle Jet • a spread of particles running roughly in the same direction as the parton after hadronization

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. 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

  32. 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

  33. Algorithm Flow Charts Clustering Algorithm Merging/Splitting Algorithm

  34. 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”

  35. 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

  36. SISCone

  37. SISCone

  38. SISCone SISCone is Infrared safe

  39. 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

  40. 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….

  41. Fast KT

  42. 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

  43. Jets

  44. Jet Finding Efficiency

  45. Response

  46. Response

  47. Jet calibration strategy

  48. 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

  49. Jet Corrections

  50. Underlying Event

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