Jun 7 – 12, 2021
Europe/Paris timezone

Extraction of the multiplicity dependence of Multiparton Interactions from LHC pp data using Machine Learning techniques

Jun 10, 2021, 6:45 PM


Experimental poster Heavy Ions Poster Session


Erik Alfredo Zepeda Garcia (Universidad Nacional Autonoma (MX))


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.

Primary authors

Erik Alfredo Zepeda Garcia (Universidad Nacional Autonoma (MX)) Antonio Ortiz Velasquez (Universidad Nacional Autonoma (MX))

Presentation materials