Speaker
Description
We present a study on the application and comparison of different machine learning algorithms for the identification of hypernuclei from simulated heavy-ion collisions, particularly those with mass number A = 3 to A = 5. The study focuses on three supervised learning algorithms - Boosted Decision Trees, Support Vector Machines and Artificial Neural Networks - which were trained to distinguish true hypernuclei candidates from combinatorial background using topological and kinematic variables of the decay products. The results demonstrate that these techniques significantly improve the background rejection of the selected hypernuclei candidates compared to traditional identification methods, thereby enhancing both the significance and precision of eventual measurements.
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