Particle Identification at BESIII Kanglin He April 23, 2007, Amsterdam

Kanglin He for BES Collaboration 2 BEPCII project e + e - multi-bunch double- ring collider Designed peak luminosity: cm -2 s 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-celled  xy = 130  m  P/P = 0.5 GeV  dE/dx = 6-7 % EM Calorimeter (EMC) ：  E/E = GeV CsI crystal array  z,  = 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 < 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 BESIII sub systemglobal combination control samples Physics Analysis 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 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 spread (~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 Two readout end Two layers

Kanglin He for BES Collaboration 10 Hadron separations Likelihood built by combining TOF and dE/dx information (~Gaussian variables) For K/ π separation, efficiency >90% and contamination rate The proton identification is extremely good at BESIII KKKK K πK π π  ππ  π π  K

Kanglin He for BES Collaboration 11 Electron-ID with EMC information E/p The “shape” of shower: E3x3/E5x5 Position matching of the EMC cluster to the charged track: ΔΦ, Δθ PDF constructed via  Fit the distribution of variables, 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 ΔθΔΦ ee ππ

Kanglin He for BES Collaboration 13 Performance Likelihood H-matrix Except Δθ, the correlations between PID variables may be as large as ~40% Network

Kanglin He for BES Collaboration 14 Neural networks Pid Multilayer Perceptrons (MLP) network implemented in ROOT Correlations of pid variables among sub detectors are 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 TOF dE/dx Network Output

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 studied in the future

Kanglin He for BES Collaboration 18 Electron-ID and muon-ID efficiencies from 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 vanished  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 in 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 applied in the future

Thank you!