Jet measurements at ATLAS. Jet measurements at ATLAS

Transcription

1 Jet measurements at ATLAS Jet measurements at ATLAS Very brief introduction to ATLAS Why jets are interesting? The ATLAS calorimetric system Jet reconstruction: from the calorimeter signal to the jet kinematics What we can do with data Conclusions

2 ATLAS at LHC ATLAS at LHC ATLAS is one of the four experiments placed on the Large Hadron Collider in Geneva. The physics program is very rich two of the main goals are: Search of the Higgs boson whose existence is predicted within the Standard Model (SM) theory: SM has been tested at Tevatron and LEP up to The origin of masses is explained by the Higgs mechanism however the Higgs boson has not been found yet The mass range that will be explored is 100 GeV 1 TeV Search for possible deviation from SM motivated from innatural high radiative corrections to the Higgs mass within SM, request for unification of coupling constants Models beyond SM for which SM is low E approximation: Super Symmetry, extra-dimensions,

3 ATLAS at the Large Hadron Collider ATLAS at the Large Hadron Collider E CM TeV Lumi cm -2 s -1 Int. Lumi/y fb -1 TeVatron Run II 1.96 < LHC(low lumi) LHC(high lumi)

4 Monday Nov

5 Why do we care about jets Why do we care about jets Jets will be the background for nearly all searches: σ(e T >100 GeV) ~ μb Signal processes: σ(nb) s (TeV) SUSY involving heavy particles: 1 TeV)~ pb SM Higgs: M=150 GeV)~ 20 pb QCD background is 5-6 order of magnitude higher than signals. Control of details of jet reconstruction and calibration is essential.

6 Jets in physics analysis Jets in physics analysis Huge QCD cross section will make Jets ubiquitous in LHC QCD is the background for nearly all searches The inclusive jet cross section measurement is one of the first measurement we can do and is by itself interesting (constraint on high x gluons PDF) 10 7 s = 14TeV StartUp: 100 pb -1 T.Le Compte s = 1. 8TeV Scaled trigger

7 Jets for discovery Jets for discovery The high pt part of the jet inclusive cross section is sensitive to new physics, i.e. quark compositness, extra dimensions These measurements requires a jet transverse momentum linearity within few percent Sensitivity to compositness L.Pribyl Most inclusive SUSY search is based on signature containing high p T (>50GeV) jets and missing transverse momenta (E T miss >100 GeV). Good control of tails.

8 Jets for precise measurements Jets for precise measurements Single lepton channel Period evts cm -2 sec -1 dm top (stat) 1 year 3x GeV Top mass measurement to 1 GeV will set the most stringet constraint on jet performance. The extimate I give here for mass shift vs jet energy scale uncertainty is naive, however it gives the correct feeling for the precision we need to aim. CDF 2006 error on top mass from JES: 1.4 GeV syst. ~50% of syst 1 week 1.9x GeV Evts = events in top mass window Top mass shift from light jet -b jet scale shift. Unconstrained fit. jet unc. 1% 5% ΔM top (jet) 0.9 GeV 11 GeV ΔM top (bjet) 0.7 GeV 3.5 GeV ATLAS Physics TDR

9 Jet production in pp collision Jet production in pp collision Beam remnants Proton Proton The event in a pp collision is composed by a soft part (Underlying Event) and by a hard interaction our detector are sensitive to both components.

10 Jet production in pp collisions Jet production in pp collisions 1. The Underlying event contribution, low Q 2 interaction, is described only by fenomenological models and will be tuned on data. 2.The hard interaction, what we are interested in, is described by perturbative QCD: sˆ = x1x2s Momentum distribution of partons in protons: Parton Distribution Function Parton cross section

11 The hard interaction The hard interaction x 1 p φ In general: x 2 p cross sections are symmetric in azimuthal angle (φ) x 1 x 2 The event is boosted along the beam axis The red part is the hard interaction that QCD knows how to describe and that we can measure through the blue part The blue part are the jets: collimated set of hadrons produced from the fragmentation of the partons emerging from the hard interaction.

12 Variables for jet kinematics Variables for jet kinematics x 1 p θ x 2 p θ Angular separations in θ are not invariant under longitudinal boosts: a given set of hadrons will appear more collimated depending on the boost. To treat equivalently partons with same p T but different boost use pseudo-rapidity (η): θ η = log tg 2 Under boost: η = η + f(x1,x2) Δη hadron i,j is invariant ΔR = ( η η ) + ( φ φ ) 2 i j 2 i j ΔR is invariant a good variable to assemble jet fragments

13 Variables for jet kinematics Variables for jet kinematics p T x 1 p x 2 p Use jet p T to discriminate between hard interaction and soft part: large momentum transfer = small distances = hard scattering large transverse energies are signal for hard interaction large energies instead do not imply hard scattering: beam remnants have huge energies but have not undergone hard scattering Jets kinematics is specified using p T, η, φ

14 Composition of the jets Composition of the jets Jets are composed by many particles which carry a low percentage of the jet energy to collect 95% of the jet energy particles down to GeV must be collected 25% of jet energy is from π GeV η = [0-1.5] M.Hodgkinson

15 Tile Calorimeters η=1.475 η=1.8 η=3.2 Cambridge T The ATLAS calorimeter The ATLAS calorimeter EM Liquid Argon Calorimeters Forward Liquid Hadronic Liquid Argon Argon Calorimeters EndCap Calorimeters Physics TDR: Jet linearity < 2% up to 4 TeV σ ( E) = E 50% 3% E( GeV ) η σ ( ET ) 100% = 10% 3 η 5 E E ( GeV ) T 3 EM accordion η < 3.2 : Pb/LAr 3 longitudinal sections 1.2 λ + preshower Δη Δϕ = and higher Central Hadronic η < 1.7 : Fe / scintillator 3 longitudinal sections 7.2 λ Δη Δϕ = and higher End Cap Hadronic 1.5 < η < 3.2 Cu/LAr 12 λ 4 longitudinal sections Δη Δϕ < Forward calorimeter 3.1 < η < 4.9 : EM Cu/LAr HAD W/Lar 3 longitudinal sections 9 λ Δη Δϕ =

16 Good for ATLAS calorimeters Good for ATLAS calorimeters Performance for the central region σ/e NIM A449(2000) σ = E 41.9% % E E linearity < 2% GeV All calorimetric systems have been tested with beams and reach the design performances. ATLAS can rely on very good hadron calorimetry with a coverage up to η <5.

17 and bad for ATLAS calorimeters and bad for ATLAS calorimeters Calorimeters are non compensated: response to em and hadron components is different with e/h ~ % variation of pion response in GeV energy range. At high energy response is ~ 80%. Epion/Eele Y.Koultchisky

18 Tile Calorimeters Electromagnetic Liquid Argon Calorimeters η=1.8 A.Dotti η=1.475 Ereco/Etruth Cracks Cracks and and dead dead material material η=3.2 Hadronic Liquid Argon EndCap Calorimeters Forward Liquid Argon Calorimeters Transition regions η Cracks and dead material in the region of transition between different systems. The variation of jet energy response as a function of prapidity is up to 30%

19 Where is the jet energy deposited? Where is the jet energy deposited? Central calorimeter region A.Gupta 2/3 of the jet energy is deposited in EM compartments

20 Jet reconstruction Jet reconstruction Reconstruction Jet reconstruction consists in obtaining from the calorimeter signals the kinematics of the particle jet or of the parton jet depending on where we want to have Theory-Data comparison. Parton jet, Particle jet, Calorimeter jets obtained running the same jet clustering algorithm on partons, stable particles after fragmentation or calorimeter signal.

21 Jet clustering algorithms Jet clustering algorithms Jet clustering algorithms must be applicable at any level: calorimeter signals, particles, partons (jet components). Various jet clustering algorithms have been implemented in ATLAS. I will describe the two that are mostly used for physics analysis: Iterative seeded cone algorithm k T algorithm Recombination Scheme Once the jet components have been obtained the jet kinematics is calculated from the compoenents applying the recombination scheme : the recombination scheme used in ATLAS is the 4-momentum sum of the jet components.

22 Jet clustering: iterative seeded cone algorithm snowmass revisited 1. All jet components in (η,ϕ) having E T > E t seed are considered as seeds for the jets ATLAS ΔR 0.4/0.7 E T seed 2 GeV ΔS 50% 2.For each seed (η s,ϕ s ) all components lying at distance <ΔR are associated to the jet CMS CDF /0.7 2 GeV 1 GeV - 75% 3. if the seed position (η s,ϕ s ) coincide (<0.05) with the jet centroide (η c,ϕ c ), the jet is considered stable otherwise (η s,ϕ s ) = (η c,ϕ c ). 4. two jets sharing a percentual of energy energy > ΔS of the least energetic jets are combined, otherwise the shared components are associated to the closest jet.

23 Jet clustering algorithm : k T Jet clustering algorithm : k T Jet components are clustered considering closness in ΔR and transverse momentum (k T ). k T jets do not have predefined geometrical shape. For each couple of components i,j d = d d ii ij = k 2 T,i = min(k 2 T,i,k 2 T,j ) D 2 ij 2 ΔR Collinear (if ΔR<<1 ) Angular resolution j j j j j j j If (d min = d ii ) jet else if d min = d ij ij (4-vector sum) in a new d ii j j j j Jet distance is ΔRij > D

24 Choice of jet algorithm and parameters Choice of jet algorithm and parameters What is the best way to choose a jet clustering algorithm for my analysis and the value of the parameters? Some examples from what has been done in ATLAS. Look at MC particles jets reconstructed with Cone and Kt and compare the reconstructed energy. With this recepy we obtain: D = 0.6 for Cone 0.7 D = 0.3 for Cone 0.4 E / KT PartJet E Cone Partjet P.Delsart

25 Tuning KT algorithm on top data Tuning KT algorithm on top data t Wb jjb N.Godbhane

26 Tunining the KT algorithm Tunining the KT algorithm W jjb

27 Choosing and tuning the jet clustering Choosing and tuning the jet clustering In ATLAS cone algorithm is more commonly used: cone 0.4 is used for top physics (crowded events) and 0.7 for other analysis (QCD, SUSY) However there is general consensus that it is very interesting to look at same physics measurement with different jet algorithm to understand pathologies, systematics i.e. QCD cross section at CDF has been measured with Cone, midpoint, k T Parameter tuning is analysis dependent however the set of default jet collection we have: cone 0.4/0.7 k T 0.4/0.6 should be a good starting point for any analysis. One more important point: kt is infrared and collinear safe while the seeded cone algorithm is not: when seeded cone jet algorithm is applied to the partons the reconstructed jets depend on soft and collinear parton emission which is not well described in fixed order QCD perturbation theory. These problems arise for example for inclusive jet cross section at NNLO.

28 Jet reconstruction phase 1: the calorimeter jet Jet reconstruction phase 1: the calorimeter jet Reconstruction Hadronic signal definition: cell energy deposits are clusterized to obtain the base objects for jet reconstruction. Noise (electronic noise and pile-up) suppression algorithms are applied at this stage.

29 Hadronic signal Hadronic signal Cells are calibrated for all calorimeters at EM scale (100 GeV electron is reconstructed as 100 GeV signal) are the input for hadronic signal reconstruction. In ATLAS we have two hadronic signal definitions: Towers of dimension Δη Δϕ = This is the most common hadronic signal definition No signal suppression is applied to avoid biases, have high contribution of only-noise cells in many towers This is the default in present ATLAS jet reconstruction 3D Energy blobs built using: 1.Cells with E >E seed to start a cluster 2.Associating 3D neighbour cells if E >Enei 3.Building a final 3D contour with cells having E >Econt Default (E seed,e nei,econt) == (4,2,0) σ

30 3D clustering (topological cluster) 3D clustering (topological cluster) The idea is that clusters assemble the cell deposits from the particle showers across the calorimeter layers Cluster for 120 GeV pion in EMEC and HEC (2002 Test Beam data) πbeam

31 Jets from Towers or Clusters Jets from Towers or Clusters Cone 0.7 jets: built from towers or from clusters Cluster naturally suppress noise Cone 0.7 jets reconstructed from Towers and clusters: calculate for each jet ΣE cell noise plot sigma vs pseudorapidity σ noise (GeV) Tower jets Cluster jets Tower jets Cluster jets I.Vivarelli

32 Jet from Towers or Clusters Jet from Towers or Clusters Hadronic signal must be small enough not drive the jet clustering algorithm. J2 Sample (35 <pt<70 GeV) High percentage of energy in Work in a single constituent, even at moderate eta progress Are Clusters extending too much or they are just following the hadronic shower developments? A.Gupta

33 Jet reconstruction phase 2: to particle jet Jet reconstruction phase 2: to particle jet Phase 2 : the detector effects are corrected calibrating the calorimeter jet particle jet What is corrected in this step: uncompensation of calorimters (e/h) effect of cracks, dead material, losses in material in fron of the calorimeters longitudinal leakage magnetic field effect. Truths contain the particles swept off from B field (p T <350 MeV do not reach the calorimeter entrances)

34 Calibrating to Particle Jet Calibrating to Particle Jet Idea: use energy density to discriminate high density EM signal from low density HAD signal. 1. Calibrated energy (Ecal) is calculated as: E EM ρcell E CAL = j = j jet = Ecell j Ecell j j w( ρcell / Vcell j, CellPosition) Ecell j Cell weighting the w(ρcell,cellposition) coefficients are obtained by minimizing the energy resolution with respect to particle jets on QCD di-jet events.

35 From the uncalibrated jet to the calibrated jet From the uncalibrated jet to the calibrated jet E E reco truth EM E E reco truth CALIB A.Gupta

36 From the Calorimeter jet to the Particle Jet From the Calorimeter jet to the Particle Jet QCD 0.7 cone jet built from calorimeter towers Uncalibrated η <0.7 15% σ ( E) = E E( GeV) 2.4 E( GeV) Calibrated η <0.7 σ ( E) = 0.02 E E( GeV) E( GeV) Linearity ±2% E>30 GeV Same algorithm on SUSY/top (q/jets, Herwig) samples give linearity within 2%

37 How How well well can can this this work work on on data data?? H8 H8 CTB CTB Setup Setup A slice of ATLAS on beam Lar modules

38 How well can this work on data? How well can this work on data? 180 GeV pion at eta=0.45 Distributions of total energy deposits in LAr, Tile and both 180 GeV pion V.Kazanine full circles are DAT histos are MC The peak description for E>10 GeV is within 1-2%

39 CTB low energy pions CTB low energy pions Low energy pions are the most interesting for jets and the most difficult π data e data T.Carli Work in progress π- 5 GeV / longitudinal shower shape highest energetic topocluster G4 One of the key ingredient to have all this working on data is to have MC describing data with high precision.

40 Phase 3: to the parton jet energy Phase 3: to the parton jet energy Phase 3: from particle to parton energy. Goal precision 1%... correct for energy losses out of jet clustering correct added energy from underlying event If theoretical prediction are availble at desired order including fragmentation (i.e. + inteface to Pythia) this extra step can be avoided and comparison are done at particle level (also make theorist happier :-) ). If theoretical prediction are only available at parton level this step is necessary

41 Using data for validation and calibration Using data for validation and calibration The first step is to use data to control all the procedure used in jet calibration in the next slides I will show one example. What data can be used to 1. validate the calibration and/or obtain parton jet scale: Single hadron tracks, MB and tau decays, check E/p W jj : imposing W mass. Maximum Energy 200 GeV, jet overlapping. Events from ttbar with 1 lepton used to trigger. Z(e + e -,μ + μ - )/ γ + jet: p T balance or Etmiss projection method. More on next slides

42 Calibration using data:p T balance Calibration using data:p T balance Method: use direct photon production: qg qγ (90%) qqbar g γ (10%) to constraint the jet p T : p T parton = p T Gamma p T Jet = p T Gamma p T gamma is reconstructed with much higher precision than the jet, can be used as reference. Gamma+jet cover a high p T range: statistical error ~1% in the central region up to 400 GeV for 100pb -1 Complications: Initial state radiation spoil the balance initial state p T 0 thus constraint is only approximately valid. High QCD background for E T <150GeV

43 Calibration with Gamma+jet events Calibration with Gamma+jet events Event selection: Gamma selection: isolation & ET>30 GeV & Highest pt jet phi back-to-back cut Δφ > p T balance = p Jet pt Photon p Photon Fit peak region iterating a gaussian fit between ±σ around the most probable value T T pt balance = (pt parton pt photon)/ pt photon pt balance = 0 perfect reproduction of parton pt from jet or compensation of unbalance from different sources pt balance > 0 pt jet > pt photon

44 Calibration using data: Gamma + jet Calibration using data: Gamma + jet Validation: comparing balance at reconstruction, particle jet and parton level gives indication on source of unbalances and deviation between MC and data: p T gamma UE Source of p T unbalance: 1. effect of ISR contribution. 2. losses due to unclustered energy. 3. contribution of UE event 4. calibration biases pt balance at parton level is within <1% ISR effect is small Cone 0.7 is correctly calibrated (red vs blue) and losses due to out of cone energy are compensated by UE (blue vs black). pt balance (ptγ+ptparton)/2 (GeV) p T jet Parton level Particle level Cone 0.7 Reconstruction level Cone 0.7 (ptγ+ptparton)/2 (GeV) 5%

45 Estimating the UE event contribution Estimating the UE event contribution To find the mean E T for UE, we consider the transverse region of the event: avoiding 60 degrees on both sides of photon and jet Mean transverse energy / tower 60 o γ jet φ Transverse region Reconstructed Particles 16.8 ± 0.3 MeV 19.9 ± 0.2 MeV Considering the number of jet components UE contribution from truth: Cone GeV Cone GeV Method to subtract UE contribution is being developed. This will allow to extract calibration parameters and obtain parton jet scale.

46 Conclusions I had to skip a lot of items: alternative calibration schemes, obtaining jet scale only using data, validation of scale in situ with single tracks, high pt jet scale, trigger A lot of work has been done however there is still a lot to do on many issues: combined test beam data understanding of topoclusters and local hadron calibration UE subtraction pile-up subtraction understanding how the in-situ calibration obtained from one channel may be transferred to other channels Performance with more realistic detector: material distortion, dead channels, non uniformities Work is continuining Data at 900 GeV will give us very interesting to understand jet reconstruction Data time is approacing, jet measurements will be soon benchmarked with real life!