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HK: Fachverband Physik der Hadronen und Kerne
HK 44: Heavy-Ion Collisions and QCD Phases IX
HK 44.2: Vortrag
Mittwoch, 13. März 2024, 16:00–16:15, HBR 62: EG 03
CBM Performance for Λ Yield Analysis using Machine Learning Techniques — •Axel Puntke for the CBM collaboration — Universität Münster
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the QCD phase diagram at high net-baryon densities (µB > 500 MeV) with heavy-ion collisions 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 Ks0 and Λ as well as for rare (multi-)strange hyperons and their antiparticles.
In this talk, the analysis of the Λ baryon yield measurement is presented. The Λ hadrons are reconstructed using methods based on a Kalman Filter algorithm that has been developed for the reconstruction of particles via their weak decay topology. The large combinatorial background is suppressed by applying selection criteria tuned to the topology of the decay. This selection is optimized by training a machine learning model based on boosted decision trees with simulated samples from two heavy-ion event generators, UrQMD and DCM-QGSM-SMM. A routine is implemented to extract multi-differentially Λ yields corrected for detector acceptance and efficiency. This analysis chain is validated by the GEANT4 Monte Carlo simulations of particle transport through the CBM detector material.
Keywords: CBM; Lambda; Strange Hadrons; Machine Learning; XGBoost