Search for new physics with missing ET at CMS

• Grant number: 23/05944-9
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Effective date (Start): June 01, 2023
• Effective date (End): May 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics - Elementary Particle Physics and Fields
• Principal Investigator: Sergio Ferraz Novaes
• Grantee: Julia Carvalho Leite
• Supervisor: Maurizio Pierini
• Host Institution: Núcleo de Computação Científica (NCC). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
• Research place: European Organization for Nuclear Research (CERN), Switzerland
• Associated to the scholarship: 22/00546-2 - Search for new Physics with missing ET with CMS, BP.DD

Abstract

The existence of physics beyond the standard model (BSM) has been hinted from neighbouring fields, like cosmology, for quite some time. Observing evidences of these new phenomena in collider experiments, or imposing limits on new theories in their absence, is one of the main challenges of contemporary high energy physics. One possible BSM scenario is that of the realization of dark matter (DM) as a particle, which could then be produced in high-energy proton collisions.One important constraint on BSM collider studies is the assumption that the posited signal of this new physics should be very subtle; extracting this signal from the standard model background usually requires new techniques to select the desired data. Deep learning techniques may offer a powerful solution to this problem, even more so in the High-Luminosity Large Hadron Collider (HL-LHC) future stage. At the HL-LHC, the luminosity available to the experimetns will increase by close to a factor of four, at the cost of increasing the mean number of events per bunch crossing from the current value of around 40 to a value between 140-200.Inevitably, the production of dark matter in proton collisions will lead to final states with missing ET . This signature is even more impacted by the HL-LHC conditions, and as such the usage of deep learning techniques may be even more crucial. (AU)