Faculty of Physics - University of Bucharest

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FACULTY OF PHYSICS

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2026-06-11 23:58

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Conference: Bucharest University Faculty of Physics 2026 Meeting

Section: High-Energy Physics

Title:

Search for new resonances in particle collisions with high final state jet multiplicity

Authors:

Gabriel MAJERI (1), Călin ALEXA (2), Daniel C. COSTACHE (1,2), Anca DINU (1,2), Ioana DUMINICĂ (1,2), Matei S. FILIP (1,2)

^*
Affiliation:

1) University of Bucharest (UB)

2) Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering (IFIN-HH)

E-mail

gabriel.majeri@unibuc.ro, calin.alexa@cern.ch, daniel.cristian.costache@cern.ch, anca-maria.dinu@cern.ch, ioana.duminica@cern.ch, matei.filip@cern.ch

Keywords:

high-energy physics, Beyond Standard Model (BSM), Monte Carlo simulations, data analysis, machine learning, ultraheavy resonances

Abstract:

Colliding elementary particles at high energies remains one of the best means we have for probing nature at its deepest levels. While we are unable to directly observe the inner processes happening in a particle accelerator, detectors provide us with large amounts of measurements for the decay products. Making sense of the raw data requires us to fit theoretical models to the detector output, which becomes complicated for processes with a large number of particle jets in the final state. Furthermore, while new investments in particle colliders, such as the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC), will increase the rate at which collisions happen, making rare events easier to find, this will also result in increased pile-up (coincidence of multiple real events occurring at the same time in the detector), again increasing the difficulty of performing data analysis and extracting statistical insights from the measurements. We argue that greater access to compute power and improvements to Monte Carlo simulations, machine learning algorithms, statistical models and overall methodology can be helpful in addressing some of these challenges. Our study focuses on estimating statistical exclusion limits for a Beyond Standard Model ultraheavy diquark resonance S_{uu}, which decays into a pair of vectorlike bosons χ, further producing hadron jets. The high multiplicity final states arise from decay chains into combinations of top quarks and W, Z and Higgs bosons. We obtain data from Monte Carlo simulations (using MadGraph, Pythia and Delphes) and then we perform signal-background classification using a machine learning-based discriminant (boosted decision trees). The statistical model is fitted on the yields using the RooFit and RooStats libraries from the ROOT framework.

References:

Halzen, F. (Francis). 1984. Quarks and Leptons : An Introductory Course in Modern Particle Physics. With Internet Archive. New York : Wiley. http://archive.org/details/quarksleptonsint0000halz.

Cottingham, W. N., and D. A. Greenwood. 2023. An Introduction to the Standard Model of Particle Physics. 2nd ed. Cambridge University Press. https://doi.org/10.1017/9781009401685.

Hanagaki, Kazunori, Junichi Tanaka, Makoto Tomoto, and Yuji Yamazaki. 2022. Experimental Techniques in Modern High-Energy Physics: A Beginner‘s Guide. Vol. 1001. Lecture Notes in Physics. Springer Japan. https://doi.org/10.1007/978-4-431-56931-2.

Collaboration, C. M. S. 2022. “Search for Resonant and Nonresonant Production of Pairs of Dijet Resonances in Proton-Proton Collisions at $sqrt{s}$ = 13 TeV.” arXiv.Org, June 20. https://doi.org/10.1007/JHEP07(2023)161.

Calafiura, Paolo, David Rousseau, and Kazuhiro Terao, eds. 2022. Artificial Intelligence for High Energy Physics. World Scientific. https://doi.org/10.1142/12200.

Duminica, Ioana, Calin Alexa, Ioan M. Dinu, Bogdan A. Dobrescu, and Matei-Stefan Filip. 2025. “Ultraheavy Diquark Decaying into Vectorlike Quarks at the LHC.” arXiv.Org, March 21. https://doi.org/10.1103/hxll-41ly.

Alwall, J., R. Frederix, S. Frixione, et al. 2014. “The Automated Computation of Tree-Level and next-to-Leading Order Differential Cross Sections, and Their Matching to Parton Shower Simulations.” arXiv.Org, May 1. https://doi.org/10.1007/JHEP07(2014)079.

Verkerke, Wouter, and David Kirkby. 2003. “The RooFit Toolkit for Data Modeling.” arXiv:physics/0306116. Preprint, arXiv, June 14. https://doi.org/10.48550/arXiv.physics/0306116.

Acknowledgement:

The work of G.M. was supported by the project ``Romanian Hub for Artificial Intelligence - HRIA'', Smart Growth, Digitization and Financial Instruments Program, 2021-2027, MySMIS no.~334906. The work of M.-S.F., C.A., D.-C.C., I.-M.D. and I.D. was supported by IFIN-HH under Contract No.~PN-23210104 with the Romanian Ministry of Education and Research. The authors would also like to thank Olivier Mattelaer of the MadGraph team for promptly responding to our bug reports and helping us understand some finer details of the Monte Carlo simulation tools. Furthermore, the authors thank the University of Bucharest for providing them with access to the high-performance computing (HPC) cluster of the University's Advanced Computing Center (ACC-UB), which was used for some of the experiments.