SMuK 2023 – wissenschaftliches Programm
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HK: Fachverband Physik der Hadronen und Kerne
HK 54: AI Topical Day – Heavy-Ion Collisions and QCD Phases XI (joint session HK/AKPIK)
HK 54.4: Vortrag
Donnerstag, 23. März 2023, 14:45–15:00, HSZ/0105
Multi-differential Λ Yield Measurement in the CBM Experiment using Machine Learning Techniques — •Axel Puntke1 and Shahid Khan2 for the CBM collaboration — 1Institut für Kernphysik, WWU Münster — 2Eberhard Karls University of Tübingen
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.
The strange 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 needs to be suppressed by applying selection criteria according to the topology of the decay. This selection is optimized by training a boosted decision tree-based machine learning model with simulated data from two event generators, UrQMD and DCM-QGSM-SMM. After the signal has been selected, the yield of the strange hadron is computed.
In this talk, the analysis procedure for the most abundant Λ baryon is presented and the performance of the non-linear multi-parameter selection method is evaluated. A fitting routine is implemented to extract the Λ yield, on which the performance gain of training a separate model for each pT-y interval will be discussed.