Correlations and Fluctuations WorkshopFirenze, July 9 th 2006 Event-by-Event physics in ALICE Chiara Zampolli ALICE-TOF Centro E. Fermi (Roma), INFN (Bologna)

Correlations and Fluctuations WorkshopFirenze, July 9th 2006

Event-by-Event physics in ALICE

Chiara ZampolliALICE-TOF

Centro E. Fermi (Roma), INFN (Bologna)

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

Firenze, July 9th 2006 Correlations and Fluctuations Workshop Chiara Zampolli

Outline

Introduction PID performance Identified Particle Spectra Particle Ratios Mean pT

Summary and Conclusions


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 minijetsDCC, Balance function...

Properties of the systemOrder of phase transitionPhysics of the QGPChiral phase transition, hadronization time...

relying on the very high particle multiplicities produced per event (SPS, RHIC, LHC)


Event by Event Fluctuations

FLUCTUATIONS

Statistical Finite number of

particles produced Experimental

acceptance and resolution

Statistical Finite number of

particles produced Experimental

acceptance and resolution

DynamicalDynamics of the

collision Evolution of the

system

DynamicalDynamics 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


Some Experimental Results

K/ratio

STAR

STAR, = 200 GeVNNs

Mean pT

NA49, = 17.2 GeV NNs

What will ALICE sensitivity be?


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 RatiosMultiplicityConserved Quantities (Charge)HBT radiiBalance FunctionFlowDCC...

http://aliceinfo.cern.ch/, ALICE PPR II

Particle IDentification plays

a crucial role!

Particle IDentification plays

a crucial role!


ALICE PID

separation @ 3 separation @ 2

(dE/dx)


Monte Carlo Event Sample

pt> 0.15 GeV/c,

-0.9 < < 0.9 K p

average # generated

6750 720 380

300 Hijing Pb-Pb events (fully simulated and reconstructed)

Centrality 0 – 10% of minbias cross section (0 < b < 5 fm)

Magnetic Field B = 0.5 T

~ 4500/dydNch


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 defectssecondaries included

Kpef

ficie

ncy

N(Prim)Prim)|N(χ

)N()|N(Prim

K p


PID Performance - Definitions

The PID performance is evaluated in terms of:

efficiency = NNt

id

wtid

wid

NN

contamination =

prim

tid

NN

overall efficiency =

wt,idN = number of correctly/uncorrectly identified particles

primN = number of generated primaries

N = number of reconstructed particles to which the PID procedure is applied


Combined PID – ITS || TPC || TOF

K

p 0.15 < pT < 4 GeV/c

K p

ID 5150 360 280

wrongly ID

155 74 13

Efficiency contamination

K P K p

98%

78%

92%

3%20%

4%

overall efficiency

K p

40% 70%

K

p


Generated vs Identified Spectra

Generated

Identified (w)

Identified (t + w)

K

p


p from weak decays

p

Generated p

Reconstructed p from

Generated p Generated Reco p from

385 130 8

Per event:


Fitting of the Spectra

Tp

expdydpNd

πpT

T

2

T2

1

Tp

expdydpNd

πpT

T

2

T2

1

Event by event fitting procedure for pT spectra: exponential function

,T = slope parameter, connected to the

Correction of the identified spectra taking into account:

Limited acceptance and reconstruction efficiency of the detectors: εacc

Transverse momentum reconstruction efficiency: εp

PID efficiency: εPID

PID contamination: CPID

(id)dydpNd

C1ε1

ε1

ε1

(reco)dydpNd

T

2

PIDPIDPaccT

2

T

kinetical freeze-out temperature


Results – Single Event, pT spectra

Temperature (MeV)

K p

186 ± 2 208 ± 8 319 ± 13

Fit range: 0.25 < pT < 2 GeV/c

K

GeneratedReconstructed

i.e. corrected!

p


Results – T Distributions

= 182 MeV

T = 3 MeVπT = 226 MeV

T = 13 MeVKT = 303

MeV

T = 21 MeV

pT

T/T ~ 0.5% T/T ~ 6% T/T ~ 7%

K p


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.


Particle Ratios

K/R = 0.106σR = 0.009R = 0.106σR = 0.009

p/ R = 0.055σR = 0.006R = 0.055σR = 0.006

σR/R ~ few %s


Mean pT, all particles

= 476 MeV

pT = 7 MeV

Tp

pT/pT ~ 1.5%

The mean value depending on the relative particle concentrations!!


Mean pT

Kp

= 451 MeV

pT = 6 MeV

πT,p = 578 MeV

pT = 24

MeV

KT,p = 744 MeV

pT = 50

MeV

pT,p

pT/pT ~ 1% p

T/pT ~ 4% p

T/pT ~ 7%


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 (, K, p) and the particle ratios (K/, p/) on an Event-by-Event basis.


Summary and Conclusions – cont’d

Temperature fluctuations: statistical fluctuations of the order of few percent for K and p.

Particle ratios: statistical fluctuations of the order of few percent for both K/ and p/

Mean pT: statistical fluctuations of the order of few percent for , 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 ~ 2÷3 in the data.E-by-E studies still feasible


Work in Progress

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

Correlations and Fluctuations WorkshopFirenze, July 9th 2006

Back-Ups


Color superconductor

B

Hadronicmatter

Critical end point

?

Nuclei

Chiral symmetrybroken

Chiral symmetryrestored

Neutron stars

T

1st order line ?

Quark-Gluon Plasma

Continuous transition for small chemical potential at:

Tc~ 170 MeVc ~ 0.7 GeV/fm3

Lattice calculations: crossover at μb~ 0

Many parameters involved

The T-μ QCD Phase Diagram

No sharp boundary between hadronic matter and QGP!!!

QCD prediction: @ 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


Experiments at the LHC

ATLAS

CMS

Designed for high pT physics in p-p

collisions

ALICE Dedicated LHC HI experiment~ 9 km

CERN

LHC


-Multiplicities & Et distributions, -HBT Correlations, elliptic and transverse flow, -hadron ratios and spectra, -Evt-by-Evt fluctuations-…

The ALICE Physics Program

-Charmonium and Bottomonium states, -strangeness enhancement, resonance modification,-jet quenching and high pt spectra, -open Charm and Beauty-thermal radiation,…

Probes of deconfinement & chiral symmetry restoration

Global characteristics of the fireball (Evt by Evt)

Heavy ion observables in ALICE

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


A Large Hadron Collider Experiment - ALICE

ITSLow pT trackingVertexing

TPCTracking, dE/dx

TRDElectron ID

TOFPID

HMPIDPID (RICH) @ high pT

PHOSγ, π0

PMDγ multiplicity

MUON μ-pairs MUON μ-pairs

= 5.5 TeV/NNDesigned for

dNch/dy|max = 8000 (optimized for 4000)Lmax = 11027 cm-

2s-1

s


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 techniqueTracking 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


ALICE Tracking Strategy

dN/dy =8000 (slice: 2o in

HMPID

TOF

TRD

TPC

ITS

• Final refit inwards

• Primary Vertex Finding in ITS

• Extrapolation and connection with outer PID detectors

After cluster finding, start iterative process through the central tracking detectors, ITS+TPC+TRD:

• Propagation to the vertex, tracking in ITS

• Back-propagation in TPC and in the TRD

• Track seeding in outer TPC


ALICE Tracking Performance

Tracking Efficiency / Fraction of Fake Tracks for dN/dy = 2000, 4000, 6000, 8000

For dN/dy = 2000 ÷ 4000,

efficiency > 90%,

fake track probability < 5%!!!

For dN/dy = 2000 ÷ 4000,

efficiency > 90%,

fake track probability < 5%!!!

Full chain, ITS + TPC + TRD


PT Resolution


ALICE Inner Tracking System – ITS

Six Layers of silicon detectors for precision tracking in ||< 0.9

• 3-D reconstruction (< 100m) of the Primary Vertex

• Tracking+Standalone reconstruction of very low momentum tracks

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

SPD - Silicon Pixel

SDD - Silicon Drift

SSD - Silicon Strip

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

Three tecnhnologies:


ALICE Time Projection Chamber – TPC

• Efficient (>90%) tracking in < 0.9

• (p)/p < 2.5% up to 10 GeV/c

Conventional TPC optimized for extreme track densities

• Two-track resolution < 10 MeV/c

• PID with dE/dx resolution < 10%

Space-Point resolution 0.8 (1.2) mm in xy,(z), occupancy from 40% to 15%


ALICE Time Of Flight – TOF

Large array at R ~ 3.7 m, covering | | < 0.9 and full

Extensive R&D, from TB data:

Intrinsic Resolution ~ 40 ps Efficiency > 99%

Readout pads 3.5x2.5 cm2

122

cm

TOF basic element: double-stack Multigap RPC

stripOccupancy < 15% (O(105)

readout channels)

2x5 gas gaps of 250mm


PID with the ITSd

E/d

x (

MIP

un

its) PID in the 1/2 region

2 measurements out of 4 Layers (SSD, SDD) used in the truncated mean

(dE/dx) ~ 10%

,K,p signals ~ gaussians

p = 0.4 GeV

dE/dx (MIP units)

p (GeV/c)

Mis-associated Clusters

central PbPb events


PID with the TPC

kaons

pions

protons

p (GeV/c)

dE

/dx (

MIP

un

its)

Use maximum signal in cluster, shared clusters not included

Truncated mean with 60% lowest signals

dE/dx (a.u.)

Well described by gaussians (@ fixed pT)

dE/dx resolution ~ 6.8% at dN/dy=8000 (5.5% for isolated tracks, or pp collisions)

Pions, 0.4<p<0.5 GeV/c

central PbPb events

Also some separationin the relativistic rise


PID with the TOF

TOF response gaussian in (tTOF – texp ),

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

• tTOF = measured time of flight

Pions

Mass (GeV/c2)

P (G

eV

/c)

Mass= p·(t2TOF/L2-1)1/2

• • k• p

Total System resolution(including track

reconstruction) ~90 ps

Mis-associated tracks


efficiency

contamination

p dependence of:

ALICE PID Performance (&)

Central Pb + Pb HIJING events – kaon case

ITSstand-alone

TPCstand-alone

TOFstand-alone

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!!!

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!!!

ITS & TPC & TOF

combined!!!


ALICE PID Approach

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

alone detectors

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

alone detectors

A common BAYESIAN approach is adopted by every ALICE detector performing PID;

The probability w(i|s) to be a particle of type i (i = e, , , ...) if a signal s (dE/dx, TOF,...) is detected, is:

π...μ,e,kk

i

Ck|srCi|sr

s|iw

r(s|i)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(s|i) from different dets


Results – T Distributions

K p

= 182 MeV

T = 4 MeVπT = 225 MeV

T = 17 MeVKT = 304

MeV

T = 22 MeV

pT

T/T ~ 2% T/T ~ 7% T/T ~ 7%

K p


Efficiency Correction Variation

K p

πT = 182 ± 1 MeV/c (was 182)

KT = 225 ± 1 MeV/c (was 225)

pT = 306 ± 2 MeV/c (was 304)

No significant change!

K p

πTσ = 3.8 MeV/c KTσ = 15.7 MeV/c pTσ = 22.6 MeV/c


Contamination Correction Variation

K p

πT = 181 ± 1 MeV/c (was 182)

KT = 227 ± 1 MeV/c (was 225)

pT = 304 ± 2 MeV/c (was 304)

No significant change!

K p

πTσ = 3.8 MeV/c KTσ = 16.0 MeV/c pTσ = 22.3 MeV/c


ITS PID

K

p

K p

ID 5200 330 270

wrongly ID

315 125 30


K P K p

97%

63%

85%

6%38%

13%

K

p

overall efficiency

K p

31% 65%


TPC PID

K

p

K P

ID 5380 220 225

wrongly ID

310 35 6


K P K p

>99%

50% 76% 6% 15% 3%

K

p

overall efficiency

K p

25% 58%


TPC || ITS PID

K

p

K p

ID 5200 310 260

wrongly ID

230 75 15


K P K p

98%

32%

85%

4%25%

6%

K

p

overall efficiency

K p

33% 65%


TOF PID

K

p K p

ID 5200 360 260

wrongly ID

100 80 10


K P K p

98%

76%

86%

2%22%

5%

K

poverall efficiency

K p

39% 66%


E-by-E Fluctuations: Observables

Mean Transverse Momentum

Mean Energy

Charge Fluctuations

Particle Ratios

Identified Particle Spectra

Particle IDentification plays a crucial role!Particle IDentification plays a crucial role!

Documents

Correlations and Fluctuations WorkshopFirenze, July 9 th 2006 Event-by-Event physics in ALICE Chiara Zampolli ALICE-TOF Centro E. Fermi (Roma), INFN (Bologna)