Tool di selezione dei dati basati su machine learning per lo studio dei fattori di forma degli iperoni per l'esperimento Belle II.

Study the internal structure of the Λ hyperon at the energy of Belle II may be possible through the channel e+e− → ΛΛ¯γISR. The reconstruction for this channel is fundamental for the scope of the project. Hence, it is crucial to investigate the reconstruction of the γISR because including its information on the final state may improve the reconstruction analysis for this channel. Whereas hyperons are unstable particles, we use a reconstruction algorithm to access the Λ particles. Since the reconstruction algorithm collects a lot of background caused by a wrong reconstruction, in my thesis I developed a Λ hyperon selector using machine learning tools aimed to improve the current reconstruction algorithm for the Λ hyperons. The analysis I conducted uses a supervised learning model, trained and tested on Monte Carlo data samples,to study which features, i.e. kinematics variables, are relevant for the selector. Thanks to the Monte Carlo Truth used as a target variable, I evaluated the performance of my Λ selector reaching an efficiency of 80% and a purity of 90% on an independent Monte Carlo sample. The work of my thesis proves the great potential of the application of machine learning tools in high energy physics experiment.