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The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) aims to investigate high-density Quantum Chromodynamics (QCD) matter by studying multi-strange baryons, charmed particles, di-leptons, and other relevant probes. A key physics observable for characterizing the hot and dense matter produced in relativistic nuclear collisions at FAIR is the identification of muon pairs produced via vector meson decays. This article presents an improvement in the reconstruction performance of Low Mass Vector Mesons (LMVMs) via their dimuon decay channel using the Gradient Boosted Decision Tree (BDTG) algorithm within a multivariate analysis framework. The results are compared with the traditional univariate cut-based method for a similar signal-to-background (S/B) ratio.

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Reconstruction of Low Mass Vector Mesons (LMVM) Using Machine Learning Techniques for CBM Experiment at FAIR SIS100

  • 会议论文

  • 发表日期：

    2025年

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  • Abhishek Kumar Sharma,
  • Raktim Mukherjee,
  • Pawan Sharma,
  • Apar Agarwal,
  • Partha Partim Bhaduri,
  • Tetyana Galatyuk,
  • Anand Kumar Dubey,
  • Anna Senger,
  • Nazeer Ahmad,
  • Subhasis Chattopadhyay

摘要

The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) aims to investigate high-density Quantum Chromodynamics (QCD) matter by studying multi-strange baryons, charmed particles, di-leptons, and other relevant probes. A key physics observable for characterizing the hot and dense matter produced in relativistic nuclear collisions at FAIR is the identification of muon pairs produced via vector meson decays. This article presents an improvement in the reconstruction performance of Low Mass Vector Mesons (LMVMs) via their dimuon decay channel using the Gradient Boosted Decision Tree (BDTG) algorithm within a multivariate analysis framework. The results are compared with the traditional univariate cut-based method for a similar signal-to-background (S/B) ratio.