Mainz 2022 – scientific programme
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HK: Fachverband Physik der Hadronen und Kerne
HK 25: Heavy-Ion Collisions and QCD Phases VI
HK 25.2: Talk
Tuesday, March 29, 2022, 16:30–16:45, HK-H2
CBM performance for (multi-)strange hadron measurements using Machine Learning techniques — •Shahid Khan1, Viktor Klochkov1, Olha Lavoryk2, Oleksii Lubynets3,4, Andrea Dubla3, and Ilya Selyuzhenkov3,5 for the CBM collaboration — 1University of Tuebingen — 2University of Kyiv — 3GSI, Darmstadt — 4University of Frankfurt — 5NRNU MEPhI, Moscow
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the QCD phase diagram at high net-baryon density (µB > 400 MeV) in the energy range of √sNN = 2.9−4.9 GeV. Precise determination of dense baryonic matter properties requires multi-differential measurements of strange hadron yields, both for the most copiously produced kaons and Λ as well as for rare (multi-)strange hyperons and their anti-particles.
This work focuses on the multi-differential reconstruction and yield of strange hadrons (Ks0, Λ, and Ξ−) using Machine Learning (ML) algorithms such as XGBoost for different collision energies. The hadrons are reconstructed via their weak decay topology using the Kalman Filter algorithm. The ML algorithms allow efficient, non-linear, and multi-dimensional selection criteria to be implemented and achieve a high signal to background ratio in the region around the invariant mass peak of the candidates. The ML algorithms are deployed and the yield extraction (multi-step fitting procedure) is implemented differentially in centrality, transverse momentum, and rapidity. Estimation of systematic uncertainties and a novel approach to study feed-down contribution to the primary strange hadrons using ML will also be discussed.