Speaker
Description
The Compressed Baryonic Matter (CBM) experiment is a fixed target heavy-ion collision experiment currently being built at the Facility for Antiproton and Ion Research (FAIR), Darmstadt, Germany.
The CBM experiment is designed to probe the QCD phase diagram at moderate temperatures and high net-baryon density via different physical observables, including flow, fluctuations, and correlations of the final state hadrons.
Reconstruction and identification of these hadrons is achieved by a combination of a silicon tracking system (STS) and particle identification detectors, specifically the transition radiation detector (TRD) and time of flight detector (TOF).
It is imperative to preserve a high level of purity in the identification of hadrons in order to investigate rare physics observables.
In this regard, a unified framework based on machine learning for the identification of hadrons by combining responses from different sub-detectors is developed.
In the first iteration, gradient-boosted decision trees (xGBOOST) are used as base models.
This contribution focuses on the implementation of the models and the recent results achieved through their application.
Furthermore, a comparison to the previously used conventional cut-based method is presented.