Extraction of multiparton interactions from ALICE pp collisions data using machine learning


  • Erik Alfredo Zepeda García ICN-UNAM
  • A. Ortiz ICN-UNAM




LHC; Multiparton Interactions; pp collisions; heavy-ion collisions; machine learning


Over the last years, Machine Learning (ML) methods have been successfully applied to a wealth of problems in high-energy physics. In this work, we discuss the extraction of the average number of Multiparton Interactions (⟨Nmpi⟩) from minimum-bias pp data at LHC energies using a regression based on Boosted Decision Trees (BDT). Using the available ALICE data on transverse momentum spectra as a function of multiplicity, we report that for minimum-bias pp collisions at √s = 7 TeV the average Nmpi is 3.98 ± 1.01, which complements our previous results for pp collisions at √s = 5.02 and 13 TeV. The comparisons indicated a modest center-of-mass energy dependence of ⟨Nmpi⟩. The study is further extended extracting the multiplicity dependence of Nmpi for the three center-of-mass energies. These results are qualitatively consistent with the existing ALICE measurements sensitives to Multiparton Interactions (MPI). Through the ML method applied to pp collisions at √s = 13 TeV, we also show that computing the multiplicity in the forward region the extraction of Nmpi is improved. This result opens the possibility to extract the number of MPI event-by-event, and in this way study the particle production as a function of MPI. Our results provide additional evidence of the presence of MPI in hadronic interactions and can help to the understanding of the heavy-ion-like behaviour observed in pp collisions data.


B. Abelev et al., Transverse sphericity of primary charged particles in minimum bias proton-proton collisions at √ s = 0.9, 2.76 and 7 TeV. Eur. Phys. J. C, 72 (2012) 2124. https://doi.org/10.1140/epjc/s10052-012-2124-9

B. Abelev et al. Multiplicity dependence of two-particle azimuthal correlations in pp collisions at the LHC. JHEP, 09 (2013) 049, https://doi.org/10.1007/JHEP09(2013)049

A. Ortiz, Experimental results on event shapes at hadron colliders. Adv. Ser. Direct. High Energy Phys., 29 (2018) 343, https://www.worldscientific.com/doi/abs/10.1142/97898132277670016

A. Ortiz, A. Paz, J. D. Romo, S. Tripathy, E. A. Zepeda, and I. Bautista, Multiparton interactions in pp collisions from machine learning-based regression. Phys. Rev. D, 102 (2020) 076014, https://link.aps.org/doi/10.1103/PhysRevD.102.076014

V. Khachatryan et al. Observation of Long-Range Near- Side Angular Correlations in Proton-Proton Collisions at the LHC. JHEP, 09 (2010) 091, https://doi.org/10.1007/JHEP09(2010)091

J. Adam et al. Enhanced production of multi-strange hadrons in high-multiplicity proton-proton collisions. Nature Phys., 13 (2017) 535, https://doi.org/10.1038/nphys4111

S. Acharya et al. Multiplicity dependence of light-flavor hadron production in pp collisions at √ s = 7 TeV. Phys. Rev. C, 99 (2019) 024906, https://link.aps.org/doi/10.1103/PhysRevC.99.024906

P. Boz˙ek, Collective flow in p-Pb and d-Pd collisions at TeV energies. Phys. Rev. C 85 (2012) 014911, https://link.aps.org/doi/10.1103/PhysRevC.85.014911

J. L. Nagle and W. A. Zajc, Small System Collectivity in Relativistic Hadronic and Nuclear Collisions, Ann. Rev. Nucl. Part. Sci., 68 (2018) 211, https://doi.org/10.1146/annurev-nucl-101916-123209

A. Ortiz, P. Christiansen, E. Cuautle Flores, I. Maldonado Cervantes, and G. Paić, Color Reconnection and Flow like Patterns in pp Collisions. Phys. Rev. Lett., 111 (2013) 042001, https://link.aps.org/doi/10.1103/PhysRevLett.111.042001

A. Ortiz and E.A. Zepeda, Extraction of the multiplicity dependence of multiparton interactions from LHC pp data using machine learning techniques. J. Phys. G, 48 (2021) 085014, https://dx.doi.org/10.1088/1361-6471/abef1e

S. Acharya et al., Charged-particle production as a function of multiplicity and transverse spherocity in pp collisions at √ s = 5.02 and 13 TeV. Eur. Phys. J. C, 79 (2019) 857, https://dx.doi.org/10.1140/epjc/s10052-019-7350-y

H. Voss, Andreas Hocker, J. Stelzer, and F. Tegenfeldt, TMVA, the Toolkit for Multivariate Data Analysis with ROOT. PoS, ACAT (2007) 040, https://doi.org/10.22323/1.050.0040

T. Sjöostrand et al., An introduction to PYTHIA 8.2. Comput. Phys. Commun., 191 (2015) 159, https://doi.org/10.1016/j.cpc.2015.01.024

R. Corke and T. Sjostrand, Interleaved Parton Showers and Tuning Prospects. JHEP, 03 (2011) 032, https://doi.org/10.1007/JHEP03(2011)032

E. Cuautle, A. Ortiz, and G. Paic, Effects produced by multiparton interactions and color reconnection in small systems. Nucl. Phys. A, 956 (2016) 749, https://doi.org/10.1016/j.nuclphysa.2016.02.031

J. Bellm et al. Herwig 7.2 release note. Eur. Phys. J. C, 80 (2020) 452, https://doi.org/10.1140/epjc/s10052-020-8011-x

A.I. Golokhvastov. Independent production and Poisson distribution. Phys. Atom. Nucl. 58 (1995) 1998

I. Antcheva et al., ROOT: A C++ framework for petabyte data storage, statistical analysis and visualization. Comput. Phys. Commun., 180 (2009) 2499, https://doi.org/10.1016/j.cpc.2009.08.005

W. H. Trzaska, New ALICE detectors for Run 3 and 4 at the CERN LHC, Nucl. Instrum. Methods Phys. Res. A 958 (2020) 162116, https://doi.org/10.1016/j.nima.2019.04.070




How to Cite

Zepeda García EA, Ortiz A. Extraction of multiparton interactions from ALICE pp collisions data using machine learning. Supl. Rev. Mex. Fis. [Internet]. 2023 Sep. 18 [cited 2024 May 29];4(2):021116 1-4. Available from: https://rmf.smf.mx/ojs/index.php/rmf-s/article/view/7030