1 Guénolé BOURDAUD -jet physics with the EMCal calorimeter of the ALICE experiment at LHC La physique des -jets avec le calorimètre EMCal de lexpérience ALICE au LHC

2 outline 1. Context 2. Previous experimental observations 3. LHC : new possibilities 4. -jet reconstruction algorithms 5. identification 6. jet reconstruction 7. Hump-backed plateau determination 8. Summary & outlooks

3 Context Quark and gluon plasma Heavy ion collisions Hard processes, jets, -jets

4 Quark Gluon Plasma Dense medium of deconfined partons LHC Nuclear mater time Initial state final state PLASMA Hadronisation Hard processes QCD predictions : Energy density > 1 GeV/fm 3 Temperature > 200 MeV Baryonic density > 5-10 x normal nuclear matter density

5 Hard probes in QGP study, historical view ~98%~ 50%~ 2% hard / tot Lessons from RHIC : Need dedicated detectors for high p T and Hard probes Alice was designed before RHIC results. EMCal QGP Initial state (partonic) observations. Explosion of hard probes JET-QUENCHING New matter state. Final state (hadronic) observations. Emergence of hard probes. Measurement Global Observables ~1994~1990~1980 Start of construction LHCRHICSPS Hard processes : Creation or diffusion of high p T particles

6 Hard processes & jets Lead to jets of particles, from hadronisation of a high p T parton high p T parton jet Parton suffers energy loss travelling through the new medium Jet multiplicity modification Jet energy redistribution Jet-quenching phenomenon Transverse plan (azimuthal) Gluon radiation Jet modification

7 -jet correlation g+q +q (Compton) q+q +g (Annihilation) Gluon radiation Jet attenuation : not perturbed by medium -jet estimates the initial jet energy -jet limits the azimuthal acceptance to search the jet Pertinent to probe QGP : A calorimeter for A tracking system (Central ALICE) for jet

8 Use -jets to study medium induced jet modification (via fragmentation function modification) Use EMCal and tracking system from LHC to reconstruct -jets Goal

9 Previous experimental observations Azimuthal correlation of hadrons First -jet study

10 RHIC Relativistic Heavy Ion Collider, BNL (USA) s NN = 200 GeV (Au-Au) s NN = 500 GeV (p-p) = 5 GeV/fm 3

11 Jet-quenching at RHIC Suppression of jet azimuthal correlation Adam et. al. Phys. Rev. Lett. 91, (2003) -hadron correlation Difficulties : direct- identification First step for -jet study di-hadron correlation STAR : T. Hallman QM2008

12 LHC : new possibilities Dedicated experiment Access to new observables

13 LHC Large Hadron Collider, CERN (Geneva) s NN = 5500 GeV (Pb-Pb) X 28 s NN = GeV (p-p) = GeV/fm 3 X 3-12

14 Central tracking + EMCal : dedicated to -jets Tracking-PID : ITS+TPC+(TOF, TRD) –Charged particles | | < 0.9 –Excellent momentum resolution up to 100 GeV/c ( p/p < 6%) –Tracking down to 100 MeV/c EMCal –Energy from neutral particles –Pb-scintillator, 13k towers – = 110, | | < 0.7 –Energy resolution ~10%/E PHOS –High resolution electromagnetic spectrometer –| | < 0.12 –220 < < 320 –Energy resolution: E /E = 3%/ E

15 -jet with ALICE Central tracking & EMCal Full jet reconstruction with tracking. Jet energy with the gamma in EMCal ~ jets/year in ALICE ( in EMCal) for E> 30 GeV Lower statistics than di-jets (4 orders of magnitude) Need the high geometrical acceptance of EMCal

16 x p-p Pb-Pb Highlight the jet energy redistribution Hump-backed plateau : distribution of the energy in the jet = ln[p T (jet)/p T (part)] Tracking Calorimeter Borghini-Wiedemann, hep-ph/

17 Feasibility with ALICE Simulation used to : Test capacity of the detectors to identify and reconstruct -jets Determine parameters of the method Test efficiency Event generator Particle propagation Detector response -jet reconstruction algorithm PYTHIA for p-p collisions HIJING for Pb-Pb simulation PYQUEN for quenching effect Possibility to force -jet events Tuneable : energy, direction… GEANT & detectors geometry Analysis framework AliRoot

18 -jet reconstruction algorithm Schematical view

19 -jet reconstruction algorithm Azimutal plan

20 -jet reconstruction algorithm 180°

21 identification Shower shape Bayesian method Efficiency

22 Particle IDentification Shower shape 0 : Cluster in EMCal Higher energy ° ° ° Gustavo Conesa : Nucl. Phys. A tower

23 Particle IDentification Bayesian method + Shower shape analysis = particle identification Shower shape Bayesian method : conditional probability : Distinguish different kind of objects, knowing the distribution of a parameter for each kind of objects. If distributions are different enough, an identification is possible. x dN/dx Bayesian method

24 0 distribution Simulation to obtain the distributions :, 0 and other hadrons are simulated with energies 5<E<60 GeV 3000 events of a single particle in EMCal acceptance 3 kinds of particles 13 energies 0 obtains from reconstructed data (ESD) Parameterization : 0 distributions are parameterized as a function of the energy Reconstruction of the PID weights for an unknown particle : From 0 distributions, unknown particle energy & 0

25 Particle IDentification hadrons °

26 0 Parametrisation for 0 Gaussian + Landau : 6 parameters Mean value of Gaussian distribution Multiplicative constant of Landau distribution 0 2 dn/d GeV

27 PID efficiency Simulation Each event contents 3 particles of each kind (, °, hadrons), with energies from 5 to 60 GeV in EMCal acceptance 3000 events mixed with p-p TeV (PYTHIA) 3000 events mixed with Pb-Pb 5.5 TeV (HIJING) Calculating unknown particle PID weights from : 0 distributions Measured energy of the unknown particle Measured 0 of the unknown particle (Method implemented in AliRoot framework)

28 0 Identification Identified : W(i) > 0,3

29 Photon identification Identified : W(i) > 0,3 Can identify photon for a -jet study !

30 -jet reconstruction candidate selection Azimuthal correlation Energy correlation Background fluctuations Jet axis determination

31 From photon to -jet candidate 1.Energy > 30 GeV (maximization direct / inclusive photons) hep-ph/ PID W,3 (only photons) 3.Isolation criteria (no decay photons) Isolation : photon without energetic particles in the photon area p-p : no particles E > 1 GeV in cone Rc = 0,4 Pb-Pb : no particles E > 3 GeV in cone Rc = 0,3 Gustavo Conesa : CERN Thesis (2005)

32 -jet : correlation The jet is emitted back-to-back with the photon in azimuthal angle 90 % of the -jets with (+/- 0.3 rad) Determined with 100 GeV -jets in p-p collisions (no background) 180°

33 -jet : energy correlation E jet / E is the fraction of reconstructed energy of the jet R C = GeV -jets

34 Pb-Pb : background Need jet energy higher than background fluctuations Jet energy Mean bkg energy bkg fluctuations E

35 Pb-Pb : background Compromise : Low R c for bkg limitation High R c to maximize E jet in cone With E jet = 30 GeV : – R c = 0,25 – E jet / (bkg) = 2

36 Jet axis reconstruction p-p Pb-Pb 100 GeV Simulation : 1 minute for a p-p collision (PYTHIA) several hundreds of particles 10 hours for a Pb-Pb collision (HIJING) several tens of thousands particles Reconstruction of -jet : Developped in AliRoot, 1GB of a daily evoluting code, no (ever) retro-compatibility.

37 Hump-Backed Plateau (HBP) determination -jet without background (p-p) : PYTHIA simulation -jet with background (Pb-Pb) : PYTHIA (signal) merged with HIJING (bkg) simulation -jet quenched : PYQUEN, processed on PYTHIA events 100 GeV -jets HBP reconstruction in p-p collisions HBP modification without background effect HBP modification with background effect HBP modification with realistic -jets

38 Hump-backed plateau in p-p collisions (100 GeV -jets) – Rc dependence – Low variations for high Rc (> 0,7) – Rc = 0,7 to determine HBP distribution

39 HBP Modification without background (100 GeV -jet) Without bkg : modification of hump-backed plateau easy to measure

40 Pb-Pb : background (100 GeV -jet) How to subtract the background ?

41 Pb-Pb : subtract background (100 GeV -jet) Background of low p T particles pollutes HBP for >3.8 Background subtraction extends HBP measurement up to = 4.2

42 Background effect (100 GeV -jet) Subtraction efficient for 1< <4,2 with 100 GeV - jets Need to test with realistic -jet energy (about 30 GeV)

43 Modification of HBP with realistic -jets Select -jets with 30<E<40 GeV Error <10% for 0.5< < 3.2 Main error contribution : background Reconstructed hump-backed plateau show the two domains : Decrease of high p T particles Enhancement of low p T particles Highlight the jet energy redistribution Realistic spectrum : 1 year LHC simulated from 30 to 100 GeV

44 Summary Particles identification : For 7<E<50 GeV : It is possible to differentiate photons, neutral pions other hadrons (efficiency ~ 50 % et purity ~ 60 %) This Method has been integrated in AliRoot framework. High p T photons are identifiable in EMCal -jets : It is possible to reconstruct and study -jets with energy higher than 30 GeV. The range for hump-backed plateau study is 0,5< <3,2 (error <10% (Pb-Pb)).

45 Particle IDentification – Add a track matching with TPC & EMCal to improve particle identification – Add an automatic procedure of 0 parametrisation -jets – Improve the background estimation – Test other algorithms for jet reconstruction – Test with a Rc dependant of the energy for jet reconstruction Outlooks

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50 from photon to gamma-jet Simulation of -jets (p-p : PYTHIA ; Pb-Pb : HIJING) PYQUEN : quenching simulation p-p : no background, Pb-Pb with background IV - -jet reconstruction in ALICE

51 Mach cone effect 3 novembre 2008 Phys.Rev. C 77, (2008)

52 jet quenching at RHIC Suppression of high p T hadrons in pp collision compared to Au-Au High p T photon suppression due to non medium effect. QM2008 R AA = d 2 N/dp T d (Au+Au) N Coll d 2 N/dp T d (p+p)

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54 EMCal tower Module (4 towers) strip-module (12 modules) super-module (24 strip-modules) EMCal (10 S-modules & 2 half S-modules) II - LHC, ALICE, EMCal

55 EMCal tower Module (4 towers) strip-module (12 modules) super-module (24 strip-modules) EMCal (10 S-modules & 2 half S-modules)

56 -jets reconstruction algorithm

57 Neutral pion desintegration

58 Background anisotropy

59 Jet energy reconstruction

60 Rc determination

61 Gamma energy resolution

62 Hadron energy reconstruction

63 Fragmentation function

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