Dark matter search with long-lived particles with the CMS experiment at the LHC

• Grant number: 20/06600-3
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2021
• End date: August 05, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics - Elementary Particle Physics and Fields
• Principal Investigator: Thiago Rafael Fernandez Perez Tomei
• Grantee: Breno Orzari
• Host Institution: Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
• Associated research grant: 18/25225-9 - São Paulo Research and Analysis Center, AP.ESP
• Associated scholarship(s): 22/07942-0 - Development of a Trigger algorithm for a dark matter search with long-lived particles with the CMS experiment at the LHC, BE.EP.DR

Abstract

Nowadays the Standard Model of particles and fields (SM) can be considered as an established theory, an statement that became even stronger after the detection of the Higgs boson. Even though the SM is the theory that better describes the phenomena that happen at the scales of subatomic particles, there are experimental observations that point to the existence of physics beyond the standard model. One of the main indications that support this statement is the observation of a number of gravitational anomalies that seem to signal the presence of a new type of matter, one that is stable, massive and practically noninteracting. The microscopic nature of this new kind of matter, named dark matter, is not yet known. The goal of this project is to search for Physics beyond the standard model with the data collected by the CMS experiment, where one could discover hints of the production of dark matter. Given the current absence of evidence of New Physics with promptly-decaying particles, we focus on models that present both dark matter candidates and long-lived particles. We propose searches with two different signatures - disappearing tracks and displaced vertices - that can arise from such models, and foresee a full analysis of the full CMS Run 2 and Run 3 combined dataset. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TOURANAKOU, MARY; CHERNYAVSKAYA, NADEZDA; DUARTE, JAVIER; GUNOPULOS, DIMITRIOS; KANSAL, RAGHAV; ORZARI, BRENO; PIERINI, MAURIZIO; TOMEI, THIAGO; VLIMANT, JEAN-ROCH. Particle-based fast jet simulation at the LHC with variational autoencoders. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, v. 3, n. 3, p. 13-pg., 2022-09-01. (20/06600-3, 18/25225-9)

ORZARI, BRENO; CHERNYAVSKAYA, NADEZDA; COBE, RAPHAEL; DUARTE, JAVIER; FIALHO, JEFFERSON; GUNOPULOS, DIMITRIOS; KANSAL, RAGHAV; PIERINI, MAURIZIO; TOMEI, THIAGO; TOURANAKOU, MARY. LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows. MACHINE LEARNING-SCIENCE AND TECHNOLOGY, v. 4, n. 4, p. 12-pg., 2023-12-01. (20/06600-3, 19/16401-0, 22/02950-5, 18/25225-9)