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This document outlines the design and implementation of the dE/dx package used for particle identification in the BESIII experiment. It details software development including calibration, algorithm studies, and systematic corrections aimed at enhancing energy loss measurements. The aim is to provide accurate particle ID information through a combination of techniques, resolving bias and improving resolution to 6–7%. Key algorithms for estimating the most probable energy loss are examined, demonstrating various statistical methods based on BESII data for reliable particle tracking and analysis.
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BESIII dE/dx package: status and algorithm studies WANG Dayong June 1,2005
Outline • dE/dx package:OO design and software development • Calibration and systematic corrections • Reconstruction algorithm studies: • Different estimation of most prob Eloss • Curve studies based on BESII data • Resolution and residual bias correction
dE/dx :Particle ID with energy loss measurements dE/dx~f(v) • Principle: P = · m Particle type info • Components: calibration and reconstruction • Implementation: C++ programming under BOSS framework • Design goal: Resolution 6—7%, good seperation MDC tracking
Requirements and data flow MDC digits • AIM: to give the partID information from the list of pulse heights of hits on the MDC track, and store them into TDS • some corrections are performed to get unbiased dE/dx information. • Some proper dE/dx estimators are constructed MDC digits Transient Data Store (TDS) MDC Tracking Tracks Tracks MDC digits Tracks dE/dx Reconstruction Recon dE/dx Recon dE/dx Recon dE/dx Global Particle Identification partId info Apparent dataflow 。。。 Real dataflow physics analysis
finally selected UML Class diagrams
Some implementation features • Uniform interface: Alternative algorithms with the same interface • Uniform data I/O format: MDC recon data model: MdcRecEvent • Input from MC : MdcFakeData package
Output:MdcDedx in MdcRecEvent • int m_id; • float m_dedx; // measured value of dE/dx • float m_dedx_exp[5]; // expected value of dE/dx for 5 particle hypotheses • float m_sigma_dedx[5]; // sigma value of dE/dx for 5 particle hypotheses • float m_pid_prob[5]; // probability for each of the 5 particle hypotheses • int m_stat; // status flag • SmartRef<MdcTrack> m_trk; // reference to the track
Calibration issues • Systematic and run-by-run calibrations is important for dE/dx correction • Calibration consts ~7200 are designed • Calib consts stored in DataBase. They can be retrieved from DB in reconstruction now • In future, calib consts in ROOT format and DB only contains meta data
dE/dx calibration and corrections • Gain variations among cells • Gas Gain variation within one cell • Sampling length corrections • Drift distance dependence • Longitude position(z) dependence • Dependence of the sense wire voltage • Space charge effect • Gas gain saturation : from electronics • Temperature,pressure and environmental effects • Corrections related to particle type • Variations of the pulse height run by run
Algorithm studies: different estimation of most probable energy loss Landau distribution has no definite mean. The algorithm used must estimate the most probable energy loss • Truncated mean • Double truncated mean: truncate at both ends • Median • Geometric mean • Harmonic mean • Transformation: • Logorithm truncated mean: studies based on BESII data idea:these methods give less bias to large values,then the satured hits have less effect to give better shape and better seperation
Different estimation of most probable energy loss: resolution(1) 5.51% 5.34% 0.05~0.75 truncation Truncation rate 0.7 6.06% 5.09% BOOST MC, MIP muon
Different estimation of most probable energy loss: resolution(2) 5.44% 5.75% Truncation rate: 0.7 5.71% 2.61% BOOST MC, MIP muon
Different estimation of most probable energy loss: seperation power(1) Pi/K Pi/P 0.7GeV 1.2GeV Pi/K Pi/P 0.7GeV 1.2GeV Pi/K Pi/P 0.6GeV 1.1GeV Pi/K Pi/P 0.75GeV 1.3GeV
Different estimation of most probable energy loss: seperation power(2) Pi/K Pi/P 0.7GeV 1.2GeV Pi/K Pi/P 0.7GeV 1.3GeV Pi/K Pi/P 0.7GeV 1.3GeV Pi/K Pi/P 0.75GeV 1.3GeV
Comparison of linear&logorithm TM • Logorithm TM(right figure),compared to plain TM(left figure): • Suppress high-end residual Landau tail • The distribution more Gaussian like BESII DATA, J/Psi hadrons shape is more Gaussian-like shape is more Gaussian-like Pull width: 1.020 0.9995 Pull width: 0.8477 0.9304 Cosmic rays Radiative Bhabha
Study of truncated mean method • Well established method of dE/dx estimation • Simple and robust • Rejection of lower end hits to remove contributions from noise and background fluctuation • Truncation of higher tail to remove Landau tail due to hard collisions Just cooresponding to ~5% lower cut Landau tail After truncation, distribution just Gaussian-like BOOST MC, 1GeV electrons
Resolution curve with different truncation rates • 70% truncation ratio is adopted for the algorthm • Number of good hits is required to no less than 10 for each track • Resolution from perfect MC consistent with empirical formula BOOST MC, 1GeV electrons
Different most probable energy loss formulations(1) • Bethe-Bloch formula • Landau formula with density correction PAI: Photo-Absorption Ionization model Sternheimer correction : Cobb-Allison correction: A
Different most probable energy loss formulations(2) B • Va’vra formulation • Other formulae
dE/dx curve studies with BesII data • Purpose: • Comparison of different formula to find the best curve to calculate expectation in reconstruction • A test-bed for BESIII reconstruction • data samples used: Pion:J/Psirho+pi & J/PsiKKPiPi Kaon:J/PsiK*(892)+K(1430)KKPiPi Proton:J/psiPPbarPi0&J/PsiPPbarEta electron: (radiative) Bhabha muon: dimu +cosmic rays “Garbage” events: beam-gas protons, cosmic-rays, rad. Bhabha Example:Cuts for Bhabha • To get pure samples: • Use Tof and BSC information ONLY to identify particles • use relative probability only • Strict kinetic and invariant mass cut • The cuts are checked with GENBES
Comparison between data and dE/dx curve • Sternheimer(A) is better at high momentum end • Va’vra(B) is relative better at low momentum end • Data need careful calibration • Practical global parameterization of curve is prefered Sternheimer B A Comparison of Sternheimer and Va’vra formula: A B
Global 5-parameter fit for phmp_nml vs • binning with nearly the same statisticsat each point to reduce the error • Using garbage events in order to fastly calibrate this curve for BESIII in future • A uniform formula to avoid discrete expression for density effect • The curve fit the BESII data OK Beam-gas proton Radiative bb Cosmic rays
Residual theta dependence before correction (Hadron events) After correction The correction is then parameterized and used in mass data process
σdE/dx~hits number relationship Empirical formula : resolution resolution J/Psi dimuon events data of different momenta bins J/Psi radiative Bhabha events number of hits number of hits
summary • OO designed BESIII dE/dx package now runs smothly under BOSS • Calibration algorithm are designed and many corrections considered • Different reconstruction algorithms are explored to get best performance • To reach design goals, there are still a long way to go
Thank you谢谢! Backed -up slides…