Multiparton interactions in pp collisions from machine learning-based regression

Abstract

Multiparton interactions (MPI) in pp collisions have attracted the attention of the heavy-ion community since they can help to elucidate the origin of collectivelike effects discovered in small collision systems at the LHC. In this work, we report that in pythia8.244, the charged-particle production in events with a large number of MPI (Nmpi) normalized to that obtained in minimum-bias pp collisions shows interesting features. After the normalization to the corresponding Nmpi, the ratios as a function of pT exhibit a bump at pT≈3 GeV/c; and for higher pT (>8 GeV/c), the ratios are independent of Nmpi. While the size of the bump increases with increasing Nmpi, the behavior at high pT is expected from the "binary scaling"(parton-parton interactions), which holds given the absence of any parton-energy loss mechanism in pythia. The bump at intermediate pT is reminiscent of the Cronin effect observed for the nuclear modification factor in p-Pb collisions. In order to unveil these effects in data, we propose a strategy to construct an event classifier sensitive to MPI using machine learning-based regression. The study is conducted using TMVA, and the regression is performed with boosted decision trees (BDT). Event properties like forward charged-particle multiplicity, transverse spherocity and the average transverse momentum (pT) are used for training. The kinematic cuts are defined in accordance with the ALICE detector capabilities. For the validation of the method and to find possible model dependence, we also compare the results from pythia8.244 with herwig7.1. In addition, we also report that if we apply the trained BDT on existing (INEL>0) pp data, i.e., events with at least one primary charged-particle within |η|<1, the average number of MPI in pp collisions at s=5.02 and 13 TeV are 3.76±1.01 and 4.65±1.01, respectively.