Speaker
Description
The Compressed Baryonic Matter (CBM) experiment at FAIR will investigate the
QCD phase diagram at high net-baryon density (μB > 500 MeV) with heavy-ion
collisions in the energy range of √sNN = 2.7−4.9 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.
The strange hadrons are reconstructed via their weak decay topology using
PFSimple, a Kalman Filter Mathematics-based package that has been developed for
the reconstruction of particles via their weak decay topology. The large combinatorial
background needs to be removed by applying certain selection criteria to the
topological features.
In this poster, selection criteria optimization for strange hadrons using the boosted
decision tree-based library XGBoost will be discussed and the performance of this
non-linear multi-parameter selection method is evaluated. To gain insights into the
importance of the different features, the trained model is analyzed by looking at the
SHAP values, which give an overview of the impact of a given feature value on the
prediction and can help to improve the accuracy of the model. As the CBM
experiment is under construction and therefore no real data is available yet, signal
and background data for the machine learning model are both taken from simulated
data of two different event generators: DCM-QGSM-SMM generates the signal
candidates and UrQMD data is treated as real data.
