Speaker
Description
Particle identification is an important ingredient to particle physics experiments. Distinguishing the charged hadrons (pions, kaons, protons and their antiparticles) is often crucial, in particular for hadronic decays which could be studied with an efficient particle identification to obtain a desirable signal-to-background ratio. An optimal performance of particle identification in a large momentum range requires an effective combination of various relevant variables provided by almost all sub-systems of a general-purpose detector. Since the particle identification capability of each variable has a complicated dependence on particle momentum and potential correlations between the other variables, it is intricate to obtain a perfect performance utilizing a predetermined equation as a model. Machine learning algorithms have been developing to use computational methods to “learn” information directly from data. Particle identification is a typical application of classification techniques of machine learning which involves a large amount of data and lots of features. The BESIII detector is used for studies of hadron physics and $\tau-$charm physics, which is composed of a helium-gas based drift chamber, a time-of-flight system, a CsI(Tl) crystal electromagnetic calorimeter and a resistive plate chamber based muon counter. High statistics and high purity data samples of pion, kaon and (anti-)proton are selected using real data accumulated in BESIII experiment, and the detector responses of these hadron samples have been investigated and summarized. These hadron samples and their characteristics of detector responses offer an unique opportunity to take advantages of machine learning techniques to make classifications or predictions. With application of gradient boosted decision trees (BDT) and usage of various features provided by all sub-detectors, the inherent potentialities of BESIII detector for particle identification is exploited and an enhancement of hadron identification capability has been achieved, especially for high momentum particles.
| Consider for long presentation | Yes |
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