Abstract
BESIII is an experiment at the high precision frontier of hadron physics in τ-charm region. Machine learning techniques have been used to improve the performance of BESIII software. In this proceeding, we present novel approaches with XGBoost for multi-dimensional distribution reweighting, muon identification and cluster reconstruction for CGEM (Cylindrical Gas Electron Multiplier) inner tracker.
Highlights
The BESIII detector is a magnetic spectrometer [1] located at the Beijing Electron Positron Collider (BEPCII) [2] which is a double ring e+e− collider running at the center of mass ene√rgies between 2.0 and 4.6 GeV and has reached a peak luminosity of 1 × 1033cm−2s−1 at s = 3770 MeV
Novel approaches with XGBoost [4] for muon identification, multi-dimensional distribution reweighting and cluster reconstruction for the Cylindrical Gas Electron Multiplier inner tracker (CGEM-IT) are presented
The cylindrical core of the BESIII detector consists of a helium-based multilayer drift chamber (MDC), a plastic scintillator time-of-flight system (TOF), a CsI(Tl) electromagnetic calorimeter (EMC) and a muon chamber system (MUC) with layers of resistive plate chambers in the iron return yoke of a 1 T superconducting solenoid
Summary
The BESIII detector is a magnetic spectrometer [1] located at the Beijing Electron Positron Collider (BEPCII) [2] which is a double ring e+e− collider running at the center of mass ene√rgies between 2.0 and 4.6 GeV and has reached a peak luminosity of 1 × 1033cm−2s−1 at s = 3770 MeV. The BESIII experiment has collected the world’s largest data samples of J/ψ, ψ(3686) and ψ(3770) decays as well as data in the energy region above 4 GeV. These data samples with unpresented precision are being used to make a varity of important and unique studies [3]. Novel approaches with XGBoost (eXtreme Gradient Boosting) [4] for muon identification, multi-dimensional distribution reweighting and cluster reconstruction for the Cylindrical Gas Electron Multiplier inner tracker (CGEM-IT) are presented
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