Multiparton Interactions in pp collisions from Machine Learning

Abstract

Over the last years, Machine Learning (ML) tools have been successfully applied to a wealth of problems in high-energy physics. In this work, we discuss the extraction of the average number of Multiparton Interactions ( 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 that for minimum-bias pp collisions at √ = 7 TeV the average mpi is 3.98 ± 1.01, which complements our previous results for pp collisions at √ = 5.02 and 13 TeV. The comparisons indicate a modest energy dependence of mpi . We also report the multiplicity dependence of mpi for the three center-of-mass energies. These results are qualitatively consistent with the existing ALICE measurements sensitives to MPI, therefore they provide additional experimental evidence of the presence of MPI in pp collisions.