Machine Learning Application for $\mathbf{\Lambda}$ Hyperon Reconstruction in CBM at FAIR

TL;DR

This paper demonstrates the use of machine learning algorithms within the Particle-Finder Simple package to efficiently reconstruct $ ext{Lambda}$ hyperons in the CBM experiment at FAIR, aiding the investigation of the QCD phase diagram.

Contribution

It introduces a machine learning-based method for $ ext{Lambda}$ hyperon reconstruction in the CBM experiment, improving signal selection and background suppression.

Findings

High signal to background ratio achieved

Effective decay topology reconstruction implemented

Enhanced $ ext{Lambda}$ hyperon detection performance

Abstract

The Compressed Baryonic Matter experiment at FAIR will investigate the QCD phase diagram in the region of high net-baryon densities. Enhanced production of strange baryons, such as the most abundantly produced $\Lambda$ hyperons, can signal transition to a new phase of the QCD matter. In this work, the CBM performance for reconstruction of the $\Lambda$ hyperon via its decay to proton and $\pi^{-}$ is presented. Decay topology reconstruction is implemented in the Particle-Finder Simple (PFSimple) package with Machine Learning algorithms providing efficient selection of the decays and high signal to background ratio.