Multiparton Interactions in pp collisions from Machine Learning

TL;DR

This paper uses machine learning to extract and analyze the average number of multiparton interactions in proton-proton collisions at LHC energies, providing new insights into their energy dependence and multiplicity behavior.

Contribution

It introduces a machine learning approach to determine the average number of multiparton interactions from experimental data, extending previous results across multiple energies.

Findings

Average N_mpi at 7 TeV is 3.98 ± 1.01

Modest energy dependence of N_mpi observed

Results align with existing ALICE measurements

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 ($\langle N_{\rm mpi} \rangle$) 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 $\sqrt{s} =$ 7 TeV the average $N_{\rm mpi}$ is 3.98 $\pm$ 1.01, which complements our previous results for pp collisions at $\sqrt{s} =$ 5.02 and 13 TeV. The comparisons indicate a modest energy dependence of $\langle N_{\rm mpi} \rangle$. We also report the multiplicity dependence of $N_{\rm mpi}$ 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.