Volume 397 - The Ninth Annual Conference on Large Hadron Collider Physics (LHCP2021) - Poster Session
Multiparton Interactions in pp collisions from Machine Learning
E.A. Zepeda Garcia* and A. Ortiz
Full text: pdf
Pre-published on: October 20, 2021
Published on: November 17, 2021
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.
DOI: https://doi.org/10.22323/1.397.0347
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