Transcript Document

PARTICLE IDENTIFICATION IN THE HADES SPECTROMETER AND PARTICLE
PRODUCTION IN C+C AT 2 A GeV
P.Tlustý17, G.Agakichiev5, C.Agodi2, H.Alvarez-Pol19, E.Atkin13, A.Balanda4, G.Bellia2,3, D.Belver19, J.Bielcik5, M.Böhmer14, H.Bokemeyer5, J.Boyard16, P.Braun-Munzinger5, V.Chepurnov6, S.Chernenko6, T.Christ14, R.Coniglione2, H.Daues5, J.Diaz20,
R.Djeridi8, F.Dohrmann18, I.Duran19, T.Eberl14, V.Emeljanov13, L.Fabbietti14, O.Fateev6, C.Fernandez19, P.Finocchiaro2, J.Friese14, I.Fröhlich8, B.Fuentes19, J.Garzon19, R.Gernhäuser14, M.Golubeva11, D.Gonzalez19, E.Grosse18, F.Guber11, J.Hehner5,
T.Heinz5, T.Hennino16, S.Hlavac1, J.Hoffmann5, R.Holzmann5, A.Ierusalimov6, I.Iori9,10, M.Jaskula4, M.Jurkovic14, B.Kämpfer18, K.Kanaki18, T.Karavicheva11, I.Koenig5, W.Koenig5, B.Kolb5, U.Kopf5, R.Kotte18, J.Kotulic-Bunta1, R.Krücken14, A.Kugler17,
W.Kühn8, R.Kulessa4, A.Kurepin11, T.Kurtukian-Nieto19, S.Lang5, J.Lehnert8, C.Maiolino2, J.Marín19, J.Markert7, Y.Mishin13, N.Montes19, J.Mousa15, M.Münch14, C.Müntz7, L.Naumann18, J.Novotný17, W.Ott5, J.Otwinowski4, Y.Pachmayer7, Y.Panebratsev6,
V.Pechenov6, T.Perez8, J.Pietraszko5, R.Pleskač17, V.Pospíšil17, W.Przygoda4, N.Rabin12, B.Ramstein16, A.Reshetin11, J.Ritman8, G.Rodriguez Prieto19, M.Roy-Stephan16, A.Rustamov5, J.Sabin Fernandez19, A.Sadovsky18, B.Sailer14, P.Salabura4, M.Sanchez19,
P.Sapienza2, A.Schmah5, C.Schroeder5, E.Schwab5, P.Senger5, R.Simon5, V.Smolyankin12, L.Smykov6, S.Spataro2, H.Stelzer5, H.Stroebele7, J.Stroth7,5, C.Sturm5, M.Sudol7,5, A.Titov6, A.Toia8, M.Traxler5, H.Tsertos15, A.Vazquez19, Y.Volkov13,
V.Wagner17, W.Walus4, Y.Wang7, S.Winkler14, M.Wisniowski4, T.Wojcik4, J.Wüstenfeld7, Y.Zanevsky6, D. Žovinec1, P.Zumbruch5
HADES@GSI
1)Institute
of Physics, Slovak Academy of Sciences, Bratislava, Slovakia 2)Istituto Nazionale di Fisica Nucleare- Laboratori Nazionali del Sud, Catania, Italy 3)Dipartimento di Fisica, Universita di
Catania, Catania, Italy 4)Smoluchowski Institute of Physics, Jagiellonian University of Cracow, Cracow, Poland 5)Gesellschaft für Schwerionenforschung , Darmstadt, Germany 6)Joint Institute of Nuclear
Research, Dubna, Russia 7)Institut für Kernphysik, Johann Wolfgang Goethe-Universität, Frankfurt, Germany 8)II.Physikalisches Institut, Justus Liebig Universität Giessen, Giessen, Germany 9)Istituto
Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy 10)Dipartimento di Fisica, Universita di Milano, Milano, Italy 11)Institute for Nuclear Research, Russian Academy of Science, Moscow,
Moscow, Russia 12)Institute of Theoretical and Experimental Physics, Moscow, Russia 13)Moscow Engineering Physics Institute (State University), Moscow, Russia 14)Physik Department E12, Technische
Universität München, Garching, Germany 15)Department of Physics, University of Cyprus, Nicosia, Cyprus 16)Institut de Physique Nucléaire d'Orsay, CNRS/IN2P3, Orsay Cedex, France 17)Nuclear
Physics Institute, Czech Academy of Sciences, Řež, Czech Republic 18)Institut für Kern- und Hadronenphysik, Forschungszentrum Rossendorf, Germany 19)Departamento de Física de Partículas.
University of Santiago de Compostela, Santiago de Compostela, Spain 20)Instituto de Física Corpuscular, Universidad de Valencia-CSIC, Valencia, Spain
Magnet
beam
The HADES spectrometer installed at GSI Darmstadt is devoted to study production of di-electron pairs from proton- and pion-induced reactions and
nucleus-nucleus collisions. Extraction of rare lepton pairs in high hadron multiplicity events requires efficient particle identification (PID). In HADES
electrons are identified by a RICH as well as a Pre-Shower and a TimeOfFlight (TOF) detector. For all charged particles momentum is measured by a
tracking system combined with a toroidal superconducting magnet, and the TOF detector provides velocity and energy loss.
The particle identification method has been implemented, allowing efficient identification of particles, using full experimental information from all
subdetectors. The basis of the method is test of hypothesis, that the reconstructed track can be identified as certain particle specie. Several measured
variables associated to each identified track from various subdetectors are used to provide a set of probabilities in individual PID algorithms, which are
then merged assuming their statistical independence. For the resulted PID probability calculation the Bayes method taking into account the prior
abundance of individual particle types, as well as the known detector response, is implemented. The performance of the method - in terms of efficiency
and purity - is then evaluated in detailed simulations.
To demonstrate the method performance, single particle spectra of charged hadrons and electrons from C+C at 2 A GeV are presented and compared with
results of corresponding simulations. In order to verify the method the proton and pion yields and transverse mass and rapidity distributions are
compared with existing data.
SHOWER(eID)
RICH
Tracking (MDC)
TOF(eID)
 e-,e+,p, identification
 real-time lepton triggering
M=1-2%@ /
 operation with p, , HI beams;
B : 0-30, T: 0-60 MeV
PID Method
PRINCIPLE:
• for each track a probability that it is of a particle type h is calculated, for all possible particle types
• Bayes theorem implemented
• cut on the resulted probability set to decide on PID
INPUT:
STEP II - Combination of measurements
STEP IV - PID decision
- merging of information from several INDEPENDENT measurements, e.g. more subdetectors. If
the
probability
density
functions
in
each
measurement
are
known,
then the likelihood to observe track with measured x for the particle type h is
1) A cut on the resulted probability is set to get PID, cut value is usually 0.5 (probability
of given particle type > 50%)
L  x | h    f k ( xk | p, h)
• for each track (track candidate) with a given momentum we have a set of independent measured
variables
• in HADES: TOF/TOFino velocity, energy loss, RICH response, MDC hit, SHOWER response
P  h | x  > cut
k
- if correlated variables are used, different approach has to be applied, e.g. principal component
analysis used in RICH p.d.f. calculation. The number of variables is reduced to number of
statisticaly independent „eigenvalues“ by diagonalizing the correlation matrix.
OUTPUT:
• a probability, that a given track corresponds to the particle type h
• efficiency and purity for a selected cut
2) For each particle type two „quality“ factors determined (from simulations):
• Efficiency defined as ratio of number of correctly identified particles and number of all
particles of given particle type in the input sample
STEP III - application of Bayes' theorem
• Purity defined for a given particle type as ratio of the number of correctly identified particles to
the number of all identifications of a given type
If probabilities of occurences of individual hypotheses == relative incident rates for each particle
type in the PID case P(h) are known (or can be estimated from both experimental data and
simulations), then
L  x | h   P h 
P h | x  
 L  x | h   P h 
STEP I - p.d.f.‘s
h  e , K , p , ,d
where P  h | x  is probability that a track with
measured x is of a type h.
Normalized probability density distributions
f k ( xk | p, h)
of each measured variable xk determined for
each particle type h
References:
There is clearly a need to take this into an
account, as it changes the decision on the
hypothesis test,
compare Fig.1 with Fig.2
• from exp data when possible (good separation
of particles)
• interpolation and extrapolation of „difficult“
regions“ with overlap from different particles
• from simulations if necessary (e.g. RICH
response)
BABAR Barlow et al. www.slac.stanford.edu/BFROOT/www/Statistics/Report/report.pdf
FOCUS hep-ex/0108011
STAR
Fisyak www.usatlas.bnl.gov/~fisyak/d0/photons/pure.ps
HERMES www.phys.ualberta.ca/~mvincter/hermes/documents/joe.ps.gz
hypothesis testing: e.g. in A.G. Frodesen, O. Skjeggestad, and H. Tofte,
Probability and Statistics in Particle Physics Columbia University Press, 1979,
ISBN 8200019063
Fig.2 Distribution of velocities for protons and deuterons at particle
momentum 750 MeV/c
Fig.1 Probability density functions of velocities for protons and
deuterons at particle momentum 750 MeV/c
Experiment and Analysis
C+C 2AGeV commissioning run - lower momentum resolution data
5% interaction target
LVL1 triggered events (Mch.>3) : 36*106 events
tracking and momentum reconstruction using inner chambers and outer
Simulation
LVL1-Trigger: 19.5 x 106 (C+C @ 2 AGeV) UrQMD events, processed through
GEANT3
Analysis - HADES data analysis package HYDRA based on C++ and ROOT
Hadron ID
Hadrons are identified mainly using velocity and momentum measurements.
e-
e+
• only tracks associated with the RICH ring are considered as lepton candidates
Purity
v/c
• RICH variables, SHOWER charge ratio and velocity used for ID
TOF
Purity
TOFino+PreSHOWER
Lepton ID
e+
Efficiency and purity of hadron identification
C+C, 2AGeV
e-
v/c
TOF/TOFino+SHOWER detectors
p10% at 0.7 GeV/c)
• leptons mainly from 0 Dalitz decay, analysis done simultaneously for EXP
and URQMD data
+
-
RICH observables
Pattern quality: Pad matrix, signal height
Pattern density: Number of pads/maxima
Pattern geometry: Ring-like shape
Efficiency
Efficiency
p
d
p*q [MeV/c]
q*p [MeV/c]
Velocity vs momentum of lepton
candidates (RICH-track)
 hadron contamination <2%
SHOWER observables
good ID up to p<1000 MeV/c, above it  not separated due to low
momentum resolution (incomplete tracking setup)
+/p
Pion yields per reaction


Post2
Post1
Pre
most of RICH observables are stromgly correlated
• Estimate of averaged number of participants Apart - URQMD events after 1st level
trigger: Apart= 7.91, in EXP number of detected charged baryons (p+d) higher by
1.083 than in SIM (p only) - different centrality selection! For EXP
Apart=7.91*1.083=8.57
• result: N / Apart

typical ring in RICH pad plane
= 0.148 ± 0.015
N = (N + N -)/2
+
• TAPS for 0 shows N / Apart = 0.138 ± 0.014
Momentum distributions
1
Qtotal
0
Qtotal
Leptons identified by producing an electromagnetic shower
● An increase in charge on post1 or post2 is an electron
signature. Charge multiplication factors
2
Qtotal
f2  0
Qtotal
Rapidity distributions
Principal Component Analysis:
2
Qtotal
f1
Q1total
0
Qtotal
simulation
experiment
This method diagonalizes the covariance matrix of the observables and yields the
eigenvectors of the distribution, which have the maximum information content. This
allows to reduce the number of dimensions without significant loss of information.
emission 4
simulation
e+
experiment
The method developement and analysis are under progress.
Presented results on leptons are obtained by combining the
probabilistic approach with a „standard“ cut method.
Shown results for both hadrons and leptons suffer from low
momentum resolution and imperfect track reconstruction
due to uncomplete detector setup.
Multiplicity distributions of leptons
counts/event (kick track>0)
e+
counts/event (kick track>0)
e-
In EXP baryons bounded in clusters (no deuterons from URQMD)
Enhancement of low mom. pions
T from fits [MeV]
mT distributions
+; experiment
; experiment
two exponents
two exponents
1 d
 C1  e  mT / T1  C2  e  mT / T2
2
mT dmT
particles from decay   N ?


•In simulation inverse slopes from UrQMD input correctly
reconstructed - no bias made by acceptance and momentum
resolution
• EXP data show two slopes shape
•Different results for + and –: acceptance and correction?
•Higher values of T of pions agree with simulation
•Agreement with KaOS results: 40 ± 3; 86 ± 3 MeV
Momentum distributions of leptons
e+
eAt present we are analyzing the data from
more recent experiment with setup
incorporating also outer MDC chambers,
i.e. data with better momentum resolution.