Köln 2025 – wissenschaftliches Programm
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HK: Fachverband Physik der Hadronen und Kerne
HK 35: Heavy-Ion Collisions and QCD Phases VI
HK 35.6: Vortrag
Mittwoch, 12. März 2025, 18:45–19:00, HS 3 Chemie
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 K0s 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. It is based on simulated events from the SMASH heavy-ion event generator, which are transported through the CBM setup using GEANT4 with subsequent detector response simulation. The Λ hadrons are then 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. A routine is implemented to extract multi-differentially Λ yields corrected for detector acceptance and efficiency. The yield extraction analysis chain is validated by comparison with the simulated data from the transport step described above.
Keywords: SMASH; Lambda; Strange Hadrons; Machine Learning; XGBoost