Event-by-Event physics in ALICE

1
Event-by-Event physics in ALICE

• Chiara Zampolli
• ALICE-TOF
• Centro E. Fermi (Roma), INFN (Bologna)

Correlations and Fluctuations in Relativistic
Nuclear Collisions, Firenze, 7th-9th July 2006
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Outline

• Introduction
• PID performance
• Identified Particle Spectra
• Particle Ratios
• Mean pT
• Summary and Conclusions

3
QGP Signatures

• The nature and the time evolution of the hot and
  dense system created in a heavy-ion collision are
  expected to show the characteristic behaviour of
  a QGP phase transition, which could dramatically
  change from one event to the other.
• Apart from the very well known probes (inclusive
  probes, probes related to deconfinement...), an
  analysis on an Event by Event basis offers the
  opportunity to study the QCD phase transition and
  to get insights into the QGP. For example

Thermodynamic quantities (T,S) Energy density
fluctuations Jets and minijets DCC, Balance
function...
Properties of the system Order of phase
transition Physics of the QGP Chiral phase
transition, hadronization time...
relying on the very high particle multiplicities
produced per event (SPS, RHIC, LHC)
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Event by Event Fluctuations
FLUCTUATIONS

• Statistical
• Finite number of particles produced
• Experimental acceptance and resolution

• Dynamical
• Dynamics of the collision
• Evolution of the system

• Sources of event-by-event fluctuations
• geometrical
• energy, momentum, charge conservation
• anisotropic flow
• Bose-Einstein correlations
• resonance decays
• jets and mini-jets
• temperature fluctuations

5
Some Experimental Results
Mean pT
K/p ratio
What will ALICE sensitivity be?
STAR
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ALICE E-by-E Program
Thanks to the very high charged particle
multiplicity expected per event, E-by-E studies
will be feasible with the ALICE detector for many
observables

• Temperature
• Mean pT
• Particle Ratios
• Multiplicity
• Conserved Quantities (Charge)
• HBT radii
• Balance Function
• Flow
• DCC
• ...

Particle IDentification plays a crucial role!
http//aliceinfo.cern.ch/, ALICE PPR II
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ALICE PID
separation _at_ 3s
separation _at_ 2s
(dE/dx)
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Monte Carlo Event Sample

• 300 Hijing Pb-Pb events (fully simulated and
  reconstructed)
• Centrality 0 10 of minbias cross section (0 lt
  b lt 5 fm)
• Magnetic Field B 0.5 T
• 4500

ptgt 0.15 GeV/c, -0.9 lt ? lt 0.9 p K p
average generated 6750 720 380
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Primary Track Selection

• The selection on primary tracks has been
  performed relying on the quality of the
  extrapolation of the tracks to the reconstructed
  primary vertex, taking into account the
  covariance parameters of the track as well.
• The inefficiency of the cut can be due to
• reconstruction defects
• secondaries included

efficiency
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PID Performance - Definitions

• The PID performance is evaluated in terms of

N number of reconstructed particles to which
the PID procedure is applied
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Combined PID ITS TPC TOF
0.15 lt pT lt 4 GeV/c
Efficiency Efficiency Efficiency contamination contamination contamination
p K P p K p
98 78 92 3 20 4
overall efficiency overall efficiency overall efficiency
p K p
74 40 70
p K p
ID 5150 360 280
wrongly ID 155 74 13
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Generated vs Identified Spectra
Generated
Identified (t w)
Identified (w)
13
p from L weak decays
p
Generated p
Reconstructed p from L
Generated p Generated L Reco p from L
385 130 8
Per event
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Fitting of the Spectra

• Correction of the identified spectra taking into
  account
• Limited acceptance and reconstruction efficiency
  of the detectors eacc
• Transverse momentum reconstruction efficiency ep
• PID efficiency ePID
• PID contamination CPID

• Event by event fitting procedure for pT spectra
  exponential function

,T slope parameter, connected to the
kinetical freeze-out temperature
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Results Single Event, pT spectra
Generated
Reconstructed
i.e. corrected!
Temperature (MeV) Temperature (MeV) Temperature (MeV)
p K p
186 2 208 8 319 13
Fit range 0.25 lt pT lt 2 GeV/c
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Results T Distributions
sT/T 0.5
sT/T 7
sT/T 6
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Systematic Uncertainties on the Corrections

• Possible sources of systematic errors
• Knowledge of the acceptance and reconstruction
  efficiencies, secondaries flow...
• A detailed study on is to be made of systematic
  uncertainties.
• Nevertheless, since a level of 10 seems
  reasonable, 100 virtual experiments randomly
  changing the efficiency (contamination)
  correction factors by 10.

• A small relative increase of few s in the width
  of the temperature distributions has been
  observed in both cases (efficiency/
  contamination).
• The mean values of the temperatures can vary by
  few s.

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Particle Ratios
K/p
R 0.106 sR 0.009
p/p
R 0.055 sR 0.006
sR/R few s
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Mean pT, all particles
476 MeV spT 7 MeV
spT/pT 1.5
The mean value depending on the relative
particle concentrations!!
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Mean pT
spT/pT 1
spT/pT 7
spT/pT 4
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Summary Conclusions

• Event by event fluctuations studies are an
  important tool to explore the QCD phase diagram,
  searching for the QGP, and the QCD critical
  point.
• Several recent experimental studies (at the SPS
  -NA49- and RHIC -STAR, PHENIX...- have focused on
  the studies of fluctuations in relativistic heavy
  ion collisions (high temperature and energy
  densities).
• Thanks to its very high particle yield per event,
  and to the excellent PID capabilities, ALICE will
  be able to study fluctuations measuring the
  identified particle spectra (p, K, p) and the
  particle ratios (K/p, p/p) on an Event-by-Event
  basis.

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Summary and Conclusions contd

• Temperature fluctuations statistical
  fluctuations of the order of few percent for p, K
  and p.
• Particle ratios statistical fluctuations of the
  order of few percent for both K/p and p/p.
• Mean pT statistical fluctuations of the order of
  few percent for p, K and p and for inclusive
  spectra.

Any other contribution from dynamical
fluctuations due to new physics will result in an
increase of the observed values

• The results presented herein strongly depend on
  the assumed dNch/dy.
• HIJING simulation dNch/dy 4500
• RHIC results suggest a reduction by a factor
  23 in the data.

E-by-E studies still feasible
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Work in Progress

• E-by-E fluctuation analysis on p-p collisions
• Multiplicity fluctuations
• Effect of Jets and Minijets

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Back-Ups
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The T-µ QCD Phase Diagram
QCD prediction _at_ very high temperatures and
energy densities, a Phase Transition from
Hadronic Matter to the QGP occurs.
What kind of phase transition? But really a phase
transition or a crossover?
LHC

• Continuous transition for small chemical
  potential at
• Tc 170 MeV
• ec 0.7 GeV/fm3
• Lattice calculations crossover at µb 0
• Many parameters involved

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Experiments at the LHC
CMS
LHC
Designed for high pT physics in p-p collisions
ALICE
Dedicated LHC HI experiment
9 km
ATLAS
CERN
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The ALICE Physics Program

• Heavy ion observables in ALICE

• Probes of deconfinement chiral symmetry
  restoration

• Global characteristics of the fireball (Evt by
  Evt)

-Multiplicities Et distributions, -HBT
Correlations, elliptic and transverse
flow, -hadron ratios and spectra, -Evt-by-Evt
fluctuations -
-Charmonium and Bottomonium states, -strangeness
enhancement, resonance modification, -jet
quenching and high pt spectra, -open Charm and
Beauty -thermal g radiation,

• p-p and p-A physics in ALICE
• Physics of ultra-peripheral heavy ion collisions
• Contribution of ALICE to cosmic-ray physics

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A Large Hadron Collider Experiment - ALICE
HMPID PID (RICH) _at_ high pT

• 5.5 TeV/NN
• Designed for
• dNch/dymax 8000
• (optimized for 4000)
• Lmax 1?1027 cm-2s-1

TOF PID
TRD Electron ID
PMD ? multiplicity
ITS Low pT tracking Vertexing
TPC Tracking, dE/dx
PHOS ?, p0
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ALICE Tracking

• Track Reconstruction has to be performed in a
  high flux environment
• Reconstruction at low pT very delicate (multiple
  scattering and energy loss)

Tracking based on a KALMAN FILTER technique

• Simultaneous reconstruction and fitting
• Rejection of incorrect space points on the fly
• Simpler handling of multiple scattering and
  energy loss effects
• Easy extrapolation from one detector to the
  other

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ALICE Tracking Strategy
After cluster finding, start iterative process
through the central tracking detectors,
ITSTPCTRD
dN/dy 8000 (slice 2o in q)

• Primary Vertex Finding in ITS

• Track seeding in outer TPC

HMPID

• Propagation to the vertex,
• tracking in ITS

TOF

• Back-propagation in TPC
• and in the TRD

TRD

• Extrapolation and connection
• with outer PID detectors

TPC

• Final refit inwards

ITS
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ALICE Tracking Performance
Tracking Efficiency / Fraction of Fake Tracks for
dN/dy 2000, 4000, 6000, 8000
Full chain, ITS TPC TRD

• For dN/dy 2000 4000,
• efficiency gt 90,
• fake track probability lt 5!!!

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PT Resolution
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ALICE Inner Tracking System ITS
Six Layers of silicon detectors for precision
tracking in ?lt 0.9
Three tecnhnologies
SPD - Silicon Pixel SDD - Silicon Drift SSD -
Silicon Strip

• 3-D reconstruction (lt 100mm) of the Primary
  Vertex

• Secondary vertex Finding (Hyperons, D and B
  mesons)

• Particle identification via dE/dx for momenta lt
  1 GeV

• TrackingStandalone reconstruction of very low
  momentum tracks

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ALICE Time Projection Chamber TPC
Conventional TPC optimized for extreme track
densities

• Efficient (gt90) tracking in ? lt 0.9
• s(p)/p lt 2.5 up to 10 GeV/c

• Two-track resolution lt 10 MeV/c
• PID with dE/dx resolution lt 10

Space-Point resolution 0.8 (1.2) mm in xy,(z),
occupancy from 40 to 15
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ALICE Time Of Flight TOF
Large array at R 3.7 m, covering ? lt 0.9 and
full f
122 cm

• TOF basic element
• double-stack Multigap RPC strip
• Occupancy lt 15 (O(105) readout channels)
  

Readout pads 3.5x2.5 cm2
2x5 gas gaps of 250mm

• Extensive RD, from TB data
• Intrinsic Resolution 40 ps
• Efficiency gt 99

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PID with the ITS
PID in the 1/b2 region
central PbPb events

• 2 measurements out of 4 Layers (SSD, SDD) used in
  the truncated mean
• s(dE/dx) 10

dE/dx (MIP units)
p (GeV/c)
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PID with the TPC
central PbPb events

• Use maximum signal in cluster, shared
  clusters not included
• Truncated mean with 60 lowest signals

protons
dE/dx (MIP units)
Also some separation in the relativistic rise
kaons
Pions, 0.4ltplt0.5 GeV/c
pions
p (GeV/c)

• Well described by gaussians (_at_ fixed pT)
• dE/dx resolution 6.8 at dN/dy8000 (5.5 for
  isolated tracks, or pp collisions)

dE/dx (a.u.)
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PID with the TOF
Total System resolution (including track
reconstruction) 90 ps
Mass p(t2TOF/L2-1)1/2
P (GeV/c)
? k p
Mass (GeV/c2)
Pions
TOF response gaussian in (tTOF texp ),

• tTOF measured time of flight

• texp time calculated from tracking
• for a given mass hypothesis

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ALICE PID Performance ()
Central Pb Pb HIJING events kaon case
Combining the PID information from different
detectors allows a weaker momentum dependence of
the efficiency (contamination) which stays higher
(lower) or at least equal than with stand-alone
detectors!!!
p dependence of
efficiency
contamination
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ALICE PID Approach

• A common BAYESIAN approach is adopted by every
  ALICE detector performing PID
• The probability w(is) to be a particle of type i
  (i e, m, p, ...) if a signal s (dE/dx, TOF,...)
  is detected, is

r(si) conditional pdf to get a PID signal s in a
detector, if a particle of type i is detected
Ci a priori probability to find a particle of
type i in the detector
Combined PID combining (multiplying) the r(si)
from different dets

• Weaker momentum dependence of the efficiency
  (contamination)
• Efficiency (contamination) higher (lower) or at
  least equal than with stand-alone detectors

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Results T Distributions
sT/T 2
sT/T 7
sT/T 7
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Efficiency Correction Variation
No significant change!
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Contamination Correction Variation
No significant change!
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ITS PID
p K p
ID 5200 330 270
wrongly ID 315 125 30
Efficiency Efficiency Efficiency contamination contamination contamination
p K P p K p
97 63 85 6 38 13
overall efficiency overall efficiency overall efficiency
p K p
73 31 65
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TPC PID
Efficiency Efficiency Efficiency contamination contamination contamination
p K P p K p
gt99 50 76 6 15 3
overall efficiency overall efficiency overall efficiency
p K p
75 25 58
p K P
ID 5380 220 225
wrongly ID 310 35 6
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TPC ITS PID
Efficiency Efficiency Efficiency contamination contamination contamination
p K P p K p
98 32 85 4 25 6
overall efficiency overall efficiency overall efficiency
p K p
74 33 65
p K p
ID 5200 310 260
wrongly ID 230 75 15
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TOF PID
overall efficiency overall efficiency overall efficiency
p K p
75 39 66
Efficiency Efficiency Efficiency contamination contamination contamination
p K P p K p
98 76 86 2 22 5
p K p
ID 5200 360 260
wrongly ID 100 80 10
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E-by-E Fluctuations Observables

• Mean Transverse Momentum
• Mean Energy
• Charge Fluctuations
• Particle Ratios
• Identified Particle Spectra

Particle IDentification plays a crucial role!