Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

HK: Fachverband Physik der Hadronen und Kerne

HK 25: Heavy-Ion Collisions and QCD Phases VI

HK 25.2: Vortrag

Dienstag, 29. März 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 Lubynets^3,4, Andrea Dubla³, and Ilya Selyuzhenkov^3,5 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 (µ_B > 400 MeV) in the energy range of √s_NN = 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 (K_s⁰, 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.