Study of the weak feed-down contamination in Lambda baryons using Machine Learning

The lightest baryon containing strangeness is the Lambda hyperon, which consists of an up- a down- and a strange valence quark. Lambda hyperons are produced and observed in heavy ion and pp collisions. There are several features of the particles that the Alice detector can measure. The distributions of the Lambda kinematic and topology variables allow in principle to disentangle between prompt and non-prompt production. This problem of disentanglement is difficult to solve. The problem is even more complicated because besides prompt and non-prompt particles, there is a background noise from various processes, and wrongly matched particles. Therefore machine learning algorithms are used to classify the data of the strange particles detected in the Alice detector to prompt, non-prompt and background.