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 ($⟨N_{\mathrm{mpi}}⟩$) 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 $\sqrt{s}=$ 7 TeV the average $N_{\mathrm{mpi}}$ 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 $⟨N_{\mathrm{mpi}}⟩$. We also report the multiplicity dependence of $N_{\mathrm{mpi}}$ 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.

arXiv commentProceedings for the poster session given at LHCP 2021

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