Speaker
Description
The Compressed Baryonic Matter (CBM) experiment, being built at the Facility for Antiproton and Ion Research (FAIR)[1] accelerator complex in Darmstadt, Germany, aims to study the phase diagram of strongly interacting matter in the realm of high net baryon densities and moderate temperatures. The SIS-100 accelerator ring at FAIR will deliver accelerated ions up to beam kinetic energies 29 GeV for protons and 11 A GeV for heavy-ions. The identification of muon pairs coming from the decay of J/ψ mesons is recognized as a crucial physics observable for analyzing the hot and dense matter formed during collisions. The Muon Chamber (MuCh)[2] detector system is being built to identify the muon pairs in a background mostly populated by muons from weak decay of pions and kaons produced in the collisions.
Simulation results on the physics performance of reconstructing for J/ψ mesons through the di-muon decay channel will be presented for p+Au collisions at 29 GeV and Au+Au collisions at 10 A GeV using various machine learning models. These results will then be compared with those obtained from traditional di-muon cut-based analysis method. The traditional cut-based approach for distinguishing signal from background relies on track selection criteria such as number of hits associated with a reconstructed global track in the Silicon Tracking System (STS), Muon Chamber (MuCh), Transition Radiation Detector (TRD), and Time of Flight (TOF) detector, along with χ² values for the Vertex, STS, and MuCh, and a 2σ TOF mass cut. These same parameters are also used for both training and testing the machine learning models. The performance of the Boosted Decision Tree Gradient (BDTG) model [3] in enhancing reconstruction efficiency (ϵ) while maintaining the Signal-to-Background ratio (S/B) will be reported.
| Category | Experiment |
|---|---|
| Collaboration (if applicable) | Compressed Baryonic Matter(CBM) collaboration |
