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Over the last years, Machine Learning (ML) methods have been successfullyapplied to a wealth of problems in high-energy physics. For instance, in aprevious work we have reported that using ML techniques one can extract theMultiparton Interactions (MPI) activity from minimum-bias pp data. Using theavailable LHC data on transverse momentum spectra as a function ofmultiplicity, we reported the average number of MPI ($\langle N_{\rm mpi}\rangle$) for minimum-bias pp collisions at $\sqrt{s}=5.02$ and 13\,TeV. Inthis work, we apply the same analysis to a new set of data. We report that$\langle N_{\rm mpi} \rangle$ amounts to $3.98 \pm 1.01$ for minimum-bias ppcollisions at $\sqrt{s}=7$\,TeV. These complementary results suggest a modestcenter-of-mass energy dependence of $\langle N_{\rm mpi} \rangle$. The study isfurther extended aimed at extracting the multiplicity dependence of $\langleN_{\rm mpi} \rangle$ for the three center-of-mass energies. We show that ourresults qualitatively agree with existing ALICE measurements sensitive to MPI.Namely, $\langle N_{\rm mpi} \rangle$ increases approximately linearly with thecharged-particle multiplicity. But, it deviates from the linear dependence atlarge charged-particle multiplicities. The deviation from the linear trend canbe explained in terms of a bias towards harder processes given the multiplicityselection at mid-pseudorapidity. The results reported in this paper provideadditional evidence of the presence of MPI in pp collisions, and they can beuseful for a better understanding of the heavy-ion-like behaviour observed inpp data.

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