Multi-Parton Interactions in pp collisions from Machine Learning-based regression

Abstract

Multi-Parton Interactions (MPI) in pp collisions have attracted the attention of the heavy-ion community since they can help to elucidate the origin of collective-like effects discovered in small collision systems at the LHC. In this work, we report that in PYTHIA 8.244, the charged-particle production in events with a large number of MPI (${\mathrm{N}}_{\mathrm{mpi}}$) normalized to that obtained in minimum-bias pp collisions shows interesting features. After the normalization to the corresponding $⟨{\mathrm{N}}_{\mathrm{mpi}}⟩$, the ratios as a function of $p_{\mathrm{T}}$ exhibit a bump at $p_{\mathrm{T}}\approx 3$ GeV/$c$; and for higher $p_{\mathrm{T}}$ ($>8$ GeV/$c$), the ratios are independent of ${\mathrm{N}}_{\mathrm{mpi}}$. While the size of the bump increases with increasing ${\mathrm{N}}_{\mathrm{mpi}}$, the behavior at high $p_{\mathrm{T}}$ 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 $p_{\mathrm{T}}$ 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 ($⟨p_{\mathrm{T}}⟩$) are used for training. The kinematic cuts are defined in accordance with the ALICE detector capabilities. In addition, we also report that if we apply the trained BDT on existing ($\mathrm{INEL}>0$) pp data, i.e. events with at least one primary charged-particle within $|\eta |<1$, the average number of MPI in pp collisions at $\sqrt{s}=5.02$ and 13 TeV are 3.76$\pm 1.01$ and 4.65$\pm 1.01$, respectively.

arXiv comment7 pages, 4 figures. The original manuscript was slightly extended, a new figure was added

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