摘要
Background Electron/positron proton separation based on Electromagnetic Calorimeter(ECAL)is crucial for the search for dark matter through precision measurement of cosmic ray positrons for the Alpha Magnetic Spectrometer(AMS-02)experiment.Proton rejection power with Boosted Decision Trees(BDT)technique in existing AMS-02 software decreases in high energy range(beyond 500 GeV),because there are fewer pure proton samples in data as background for BDT training,and proton Monte Carlo(MC)simulation shows disagreement in ECAL energy distribution compared to the data.Purpose Improve the proton rejection power based on ECAL in high energy range.Method Tuning the distribution of the variables of proton MC used in BDT to agree with the proton data.Using proton MC as background training sample with a two-step BDT training approach.Results The proton rejection power above 1.0 TeV is increased to,representing an improvement by a factor of 5 compared to 12×10^(4)the BDT in existing AMS-02 software.
基金
supported by National Key R&D Program of China(2022YFA1604802,2022YFA1604803)
National Natural Science Foundation of China(11905238)
the China Scholarship Council(202204910318).
