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


Abstract in English

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 LHC data on transverse momentum spectra as a function of multiplicity, 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. In this 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 pp collisions at $sqrt{s}=7$,TeV. These complementary results suggest a modest center-of-mass energy dependence of $langle N_{rm mpi} rangle$. The study is further extended aimed at extracting the multiplicity dependence of $langle N_{rm mpi} rangle$ for the three center-of-mass energies. We show that our results qualitatively agree with existing ALICE measurements sensitive to MPI. Namely, $langle N_{rm mpi} rangle$ 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 behaviour observed in pp data.

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