Extraction of the multiplicity dependence of multiparton interactions from LHC pp data using machine learning techniques

Over the last years, machine learning (ML) methods have been successfully applied to a wealth of problems in high-energy physics. For instance, in a previous work we have reported that using ML techniques one can extract the multiparton interactions (MPI) activity from minimum-bias pp data. Using the available large hadron collider data on transverse momentum spectra as a function of multiplicity, we reported the average number of MPI (< N-mpi >) for minimum-bias pp collisions at f = 5.02 and 13 TeV. In this work, we apply the same analysis to a new set of data. We report that < N-mpi > amounts to 3.98 +/- 1.01 for minimum-bias pp collisions at root S = 7 TeV. These complementary results suggest a modest center-of-mass energy dependence of < N-mpi >. The study is further extended aimed at extracting the multiplicity dependence of < N-mpi > for the three center-of-mass energies. We show that our results qualitatively agree with existing ALICE measurements sensitive to MPI. Namely, < N-mpi > increases approximately linearly with the charged-particle multiplicity. But, it deviates from the linear dependence at large charged-particle multiplicities. The deviation from the linear trend can be explained in terms of a bias towards harder processes given the multiplicity selection at mid-pseudorapidity. The results reported in this paper provide additional evidence of the presence of MPI in pp collisions, and they can be useful for a better understanding of the heavy-ion-like behavior observed in pp data.