Speaker
Description
Over the last years, Machine Learning (ML) tools have been successfully applied to a wealth of problems in high-energy physics. In this talk, we will discuss the extraction of the average number of Multiparton Interactions ($〈N_{mpi}〉$) from minimum-bias pp data at LHC energies using ML methods. Using the available ALICE data on transverse momentum spectra as a function of multiplicity we report the $〈N_{mpi}〉$ for pp collisions at √s = 7 TeV, which complements our previous results for pp collisions at √s = 5.02 and 13 TeV. The comparisons indicated a modest energy dependence of $〈N_{mpi}〉$. We also report the multiplicity dependence of $N_{mpi}$ for the three center-of-mass energies. These results are fully consistent with the existing ALICE measurements sensitives to MPI, therefore they provide experimental evidence of the presence of MPI in pp collisions.
