Particle Identification at BESIII

Kanglin He

hekl@ihep.ac.cn

April 23, 2007, Amsterdam

Kanglin He for BES Collaboration 2

BEPCII project

e+e- multi-bunch double-ring collider

Designed peak luminosity: 1033cm-2 s-1@1.89GeV

Physics: Charmonium Physics (J/Ψ,Ψ(2s) ), Light Hadron Spectrocopy, D/Ds Physics, QCD/R Value measurements, tau physics etc.

Scheduled to provide collisions in summer , 2008.

Kanglin He for BES Collaboration 3

BESIII Detector

Muon Chamber (MUC) ：RPC based

　

TOF System ：T = 90 ps barrel 110 ps endcap

Main Drift Chamber (MDC) ：Helium based small-celledxy = 130 mP/P = 0.5 %@1 GeVdE/dx = 6-7 %

EM Calorimeter (EMC)： E/E = 2.5 % @ 1 GeV　 CsI crystal array z, = 0.6 cm @ 1 GeV

Super-conducting Magnet ： 1.0 Tesla

Kanglin He for BES Collaboration 4

Particle ID system at BES3 Tof

Two layer barrel time-of-flight, time resolution ~90ps 1 layer endcap TOF, time resolution ~110ps Q of two layer barrel TOF may provide additional PID info.

dE/dx Resolution ~(6-7)%, 3σ K/π separation up to 600MeV

Emc CsI (Tl) crystal Deposit energy, “shape” of shower

Muc cut off momentum, lower to 450 MeV μ-ID efficiency > 95%, π punch-through < 3% @ 1GeV

Provide good e/μ/π/K/p separation in large Solid angle coverage of BES3 detector

Kanglin He for BES Collaboration 5

Offline software system Framework

GAUDI (originally developed by LHCb) Simulation

GEANT4 Reconstruction

Adopt lots of code from Belle, BaBar, ATLAS, GLAST … Calibration Database

Mysql Analysis

Particle identification Kinematic/Vertex fit Partial wave analysis, Dalitz plot analysis etc

Amount of work has been accomplished but much remains to be done

Kanglin He for BES Collaboration 6

Pid algorithm at BESIIILikelihoodNetwork

LikelihoodNetwork

LikelihoodNetwork

LikelihoodNetwork

Likelihood, networkTOF

dE/dx

EMC

MUC

p

K

p

e

p, p

xy

sub system global combination

cont

rol s

ampl

es

Phy

sics

Ana

lysi

s

cuts

Kanglin He for BES Collaboration 7

The dE/dx system

Hit level calibration Q normalization in the

partitions of drift distance and entry angle

The analysis of cosmic ray data is in progress

Track level calibration Amount of work has been

done based on the MC simulated data

A lot of work have to be done in the future (waiting for the real data)

Kanglin He for BES Collaboration 8

TOF calibration

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243

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zppttcor

An empirical formula (BESII) is applied to each readout unit

Time resolution varied with hit position

Kanglin He for BES Collaboration 9

Correlations between TOF measurements The contribution of beam spre

ad (~40ps) to TOF measurements is sizable compared to the intrinsic resolution

The correlations between TOF measurements can be obtained from calibration data set, e.g., Bhabha events

The weighted combination of two layer TOF is required in BESIII pid algorithm

The systematic offsets for hadrons could be corrected by the experiences of BESII

Tw

o re

adou

t en

dT

wo

laye

rs

Kanglin He for BES Collaboration 10

Hadron separations Likelihood built by combining TOF and dE/dx information (~G

aussian variables) For K/ π separation, efficiency >90% and contamination rate

<10% @1GeV/c The proton identification is extremely good at BESIII

KK

K π

π π

π K

Kanglin He for BES Collaboration 11

Electron-ID with EMC information E/p The “shape” of shower: E3

x3/E5x5 Position matching of the E

MC cluster to the charged track: ΔΦ, Δθ

PDF constructed via Fit the distribution of variabl

es, cell analysis on the basis of likelihood method

H-Matrix method, investigate the correlations between variables

e

π

E/p ratio of e, π(0.8—0.9 GeV/c)

Kanglin He for BES Collaboration 12

Δθ ΔΦ

e e

π π

Kanglin He for BES Collaboration 13

Performance

Like

lihoo

dH

-mat

rix

Except Δθ, the correlations between PID variablesmay be as large as ~40% Network

Kanglin He for BES Collaboration 14

Neural networks Pid Multilayer Perceptrons (MLP) network implemented i

n ROOT Correlations of pid variables among sub detectors ar

e reasonable small Allow us to configure the network sequentially Make the systematical checks easily

The configuration of networks Each sub-detector has one output variable

Networks are small and simple The output of sub-detector (sub-network) can be combined

in several ways: PDF of resulting variables for likelihood analysis As input variables for a sequential network

Kanglin He for BES Collaboration 15

Results of TOF and dE/dx networks

TO

Fd

E/d

x Ne

two

rk O

utp

ut

Kanglin He for BES Collaboration 16

Results of EMC network

Kanglin He for BES Collaboration 17

Results of MUC network

Information of muon track and position matching will be studiedin the future

Kanglin He for BES Collaboration 18

Electron-ID and muon-ID efficienciesfrom sequential networks

Excellent electron-ID is expected at BESIII in full momentum ranges It’s interesting that the acceptance hole between 0.2—0.4 GeV/c va

nished Combined contribution from sub detectors (dE/dx+TOF+EMC)

Muon-ID efficiency is ~90%, the pion contamination rate is ~10% at low momentum range and ~5% above 1 GeV/c (MUC+EMC) More detail studies are needed in the future

Kanglin He for BES Collaboration 19

Summary The Pid software are still under studying

Reconstruction/calibration and the analysis algorithm Currently, the likelihood method and neural network are studied i

n parallel at BESIII Sub-detector level and global combination

The likelihood method worked well in dE/dx and TOF system The correlated analysis was applied in TOF PID

The network did better in muon-ID Further improvements are expected by exploring more useful PID

variables The sequential network worked well in electron and muon ID The final decision of global combination method is not made

Likelihood or sequential network Other powerful algorithm, e.g., boosted decision tree, may be ap

plied in the future

Thank you!