Speaker
Description
The ALICE Collaboration aims to precisely measure heavy-flavour (HF) hadron production in high-energy proton-proton and heavy-ion collisions since it can provide valuable tests of perturbative quantum chromodynamics models and insights into hadronization mechanisms. Measurements of the Ξ$_c^+$ and Λ$_c^+$ production decaying in a proton (p) and charged π and K mesons are remarkable examples of investigation in the HF sector. Like in other ALICE analyses, a quite novel approach based on Boosted Decision Tree (BDT) classifiers has been adopted to discriminate the signal yields from the background processes. Especially for Ξ$_c^+$ → pπK process, the Machine Learning (ML)-based approach is required and particularly challenging due to its large combinatorial background, small branching ratio, and short O(100 µm) decay length of Ξ$_c^+$ baryon. FAIR, a European project synergic to the ALICE experiment, aims to set up an open-source, user-friendly and interactive pytorch-based environment external to the official ALICE framework to perform BDT-based multivariate analyses. The FAIR benchmark imports different ML packages (XGBoost, Sklearn and Ray) to prepare the data and configure the BDT models in Jupyter Notebooks. Currently, the training is performed on a preliminary dataset with limited statistics using a partitioned shared GPU available through an Apache Mesos cluster at the ReCaS-Bari datacenter. In the future, when a larger dataset will be available, we intend to leverage a GPU-powered Kubernetes cluster for processing large-scale applications, including ML tool training. This contribution will present a performance comparison of the investigated BDT architectures trained with simulated signal events and background Run 3 data provided by ALICE.
