Transverse momentum spectra using the Inner Tracking System of the ALICE experiment at LHC

Transcription

1 - 1 - Transverse momentum spectra using the Inner Tracking System of the ALICE experiment at LHC Seminario di fine secondo anno di dottorato Torino, 3 Febbraio 2010

2 Outline

3 A Large Ion Collider Experiment LHC -3 Pb-Pb collisions at s = 5.5 TeV per nucleon study in detail the behavior of nuclear matter at high densities and temperatures (Quark Gluon Plasma) p-p collisions at s = 14 TeV p-p Physics using particular features reference for Pb-Pb

4 The Inner Tracking System Silicon s pixel (SPD) 240 sensitive volumes drift () 260 sensitive volumes strip double-side (SSD) 1698 sensitive volumes Resolution Detector x loc [µm] z loc [µm] occupancy [%] SPD SSD total material budget 0.07 X 0

5 ALICE Silicon Drift Detector Anodes Cathodes Anode pitch 294 µm Cathode pitch 120 µm HV (nominal) V

6 ALICE Silicon Drift Detector Anodes Cathodes Anode pitch 294 µm Cathode pitch 120 µm HV (nominal) V

7 The module -6 front back

8 The s inside the ITS - 7 -

9 Physical motivations of ITS Primary vertex reconstruction Complement TPC tracking Heavy flavor Physics Track impact parameter

10 first p-p collision in ALICE! p-p events collected until now: 800 s = 900 GeV 200 s = 2.36 TeV

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12 : drift time dependence Electron cloud, generated in the Si, spreads during the drift Signal tails could be cut by the zero-suppression algorithm How to study this effect? For each layer, charge distributions plotted in drift time bins and fitted by Landau+Gaussian convolution Fit parameter plotted versus drift time

13 : drift time dependence Landau+Gaussian convolution fit in drift time bins (an example) y=counts, x=cluster charge [kev] drift time range from 0 to 5.6 µs simulation

14 : correction for zero suppression p-p events simulation cosmic data in Turin

15 : correction for zero suppression p-p events simulation cosmic data in Turin p-p 900 GeV correction extracted from simulation applied to data no dependence on drift time

16 ADC-to-keV module by module ADC to kev conversion factor has to be set module by module tuning parameters obtained from p-p 900 GeV 95% of modules (MPV) < 10%

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18 Particle Identification in ITS and TPC Energy loss information used to identify particles Value calculated by using a truncated mean (on 4 points in ITS, 90 points in TPC) Possibility to distinguish p, K and π only In TPC, better p t resolution due to the bigger lever arm

19 Features of ITS as standalone tracker Efficiency Resolution The standalone ITS can: 1 recover the tracks miss by the TPC 2 identify particles at low momentum

20 First results with 900 GeV p-p data: energy loss in ITS

21 First results with 900 GeV p-p data: energy loss in ITS

22 First results with 900 GeV p-p data: energy loss in ITS

23 Bayesian : the formula For each track, we know: 1 energy loss by the particle in the 2 particle momentum how to understand what kind of particle generate the track?

24 Bayesian : the formula For each track, we know: 1 energy loss by the particle in the 2 particle momentum how to understand what kind of particle generate the track? Bayesian probability for a track of momentum p, with measured de/dx, to be of type i: R(S i)p(i) P(i S) = P t=p,k,π R(S t)p(t) where S=dE/dx, P(i) are the priors, R(S) are the response functions. For priors we can use a recursive method: first step: P(i) = 1 for π,k,p 3 next steps: P (i)= N i N tot, where N i particles with P(i S) > threshold

25 Bayesian : the algorithm For the evaluation of the response functions we need a multi-step procedure: 1 generate p-p event in ALICE (PYTHIA) 2 reconstruct tracks with 4 points (clusters) in and SSD 3 measure the deposited energy in the 4 layers (+SSD) for each particle specie

26 Bayesian : the algorithm For the evaluation of the response functions we need a multi-step procedure: 1 generate p-p event in ALICE (PYTHIA) 2 reconstruct tracks with 4 points (clusters) in and SSD 3 measure the deposited energy in the 4 layers (+SSD) for each particle specie 4 For each momentum bin, de/dx distribution for p, K, π (i.e. R(S)) fitted by Landau-Gaussian convolution Example layer 4 particle π P [320 MeV,352 MeV] estimation of resolution WL 2 + WG 2 16keV

27 Fit parameters: p-p events, layer 3, pions

28 Fit parameters: p-p events, layer 5 SSD, kaons

29 First results for Bayesian (momentum bins) (π,k,p) de/dx distributions obtained from: 1 simulation using Montecarlo truth 2 simulation using Bayesian

30 First results for Bayesian (momentum bins) (π,k,p) de/dx distributions obtained from: 1 simulation using Montecarlo truth 2 simulation using Bayesian 3 DATA using Bayesian

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32 Physical motivation for momentum spectra Momentum spectra from p-p event can be used to: validate theoretical models (PYTHIA, PHOJET) reference for Pb-Pb analysis new Physics? (DATA from LHC first paper)

33 First spectra using standalone ITS and the Bayesian SIMULATION: comparison between on left, spectra performed with the Montecarlo truth on right, spectra performed using the Bayesian (π,k,p) contamination for kaons at high p t has to be corrected

34 First spectra using standalone ITS and the Bayesian DATA: spectra performed using p-p events at s = 900 GeV (raw spectra) (π,k,p) Long long way... to correct the spectra, taking into account: efficiency (in particular for kaons) secondary decays (in particular for protons)

35 contamination and efficiency: kaons ( 8 < : efficiency = N good N true contamination = N(π π) = N(π K) = N(π p) = N fake N identified true π identified as π true π true π identified as K true π true π identified as p true π kaons 300 k entries p-p events ITS standalone

36 Secondaries for each specie all secondaries: strange and material only secondaries from strange

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38 Publications Published 1 Characterization of the ALICE Silicon Drift Detectors using an infrared laser Journal of Instrumentation, JINST 3 P06004, collaboration (master degree thesis work) 2 First proton proton collisions at the LHC as observed with the ALICE : measurement of the charged particle pseudorapidity density at s = 900 GeV European Physical Journal C, arxiv: v2, ALICE collaboration (first LHC paper!) 3 Charge collection in the Silicon Drift Detectors of the ALICE experiment Journal of Instrumentation, arxiv: v1, collaboration (corresponding author) Conference proceedings 1 Commissioning of the Silicon Drift Detectors of th ALICE LHC XLVII International Winter Meeting on Nuclear Physics at Bormio, arxiv: v1 Submitted 1 Alignment of the ALICE Inner Tracking System with cosmic-ray tracks Journal of Instrumentation, arxiv: v2, ALICE collaboration 2 Operation and of the Silicon Drift Detectors of the ALICE experiment during the 2008 cosmic ray data taking period Journal of Instrumentation, arxiv: v1, collaboration

39 Past and future s To do are calibrated and ready to be used in the standalone ITS analysis the Bayesian algorithm is ready included in the global ALICE task force improve the correction framework to correct raw spectra obtain dn/p t, dn/dy distributions of identified particles for p-p at several energies (0.9, 2.36, 7 TeV) and Pb-Pb INFN fellow at CERN for the 2010 analysis: spectra in the standalone ITS hardware: Data Acquisition expert on call for

40 That s all, thanks. I wish to thank the people of the ALICE group of Torino for the continuous and frequent support in my work: F. Prino, S. Beolé, L. Ramello, M. Masera, P. Giubellino, L. Riccati, G. Ortona, M. Monteno, B. Alessandro, S. Senyukov, M. Sitta, M. Poghosyan and R. Bala.

41 backup slides

42 First results with 900 GeV p-p data: energy loss in ITS particles tracked both in TPC and ITS 2 signal calculated using weighted average mean of 4 signal in ITS 3 tuned using different cuts: track χ 2 number of points in ITS layers 1 4 comparison with the theoretical Bethe-Bloch function for different particles 1 6 bits code: ITSclusterMaps>

43 Bayesian : energy resolution

44 Contamination and efficiency: pions pions 300 k entries p-p events ITS standalone