Speaker
Description
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the QCD phase diagram at high net-baryon density ($\mu_{B} > 400\ \textrm{Me}V$) in the energy range of $\sqrt{s_{NN}} = 2.9−4.9\ \textrm{Ge}V$. Precise determination of dense baryonic matter properties requires multi-differential measurements of strange hadron yields, both for most copiously produced kaons and $\Lambda$ as well as for rare (multi-)strange hyperons and their anti-particles.
In this presentation, the CBM performance for the multi-differential yield measurements of strange hadrons ($K_{s}^{0}$, $\Lambda$, and $\Xi^{-}$) will be reported. The strange hadrons are reconstructed via their weak decay topology using the Kalman Filter algorithm. Machine Learning techniques, such as XGBoost, are used for non-linear multi-parameter selection of weak decay topology, resulting in high signal purity and efficient rejection of the combinatorial background. Yield extraction and extrapolation to unmeasured phase space is implemented as a multi-step fitting procedure, differentially in centrality, transverse momentum, and rapidity at different collision energies. Variation of the analysis parameters allows estimating systematic uncertainties. A novel approach to study feed-down contribution to the primary strange hadrons using Machine Learning algorithms will also be discussed.
