Advanced Statistical Techniques for Dark Matter Searches with Gamma-ray Observatories

• Grant number: 25/08438-2
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 01, 2025
• End date: August 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Physics - Elementary Particle Physics and Fields
• Principal Investigator: Luiz Vitor de Souza Filho
• Grantee: Micael Jonathan Duarte Andrade
• Supervisor: James A Hinton
• Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Institution abroad: Max Planck Society, Heidelberg, Germany
• Associated to the scholarship: 23/15494-0 - Optimization of Indirect Dark Matter Searches with Ground-based Gamma-Ray Observatories, BP.DR

Abstract

The nature of dark matter (DM) remains one of the most compelling mysteries in modern physics. Indirect detection using very-high-energy gamma rays is a promising approach to unveil the particle nature of DM. In particular, the Galactic Center (GC) region of the Milky Way, with its high DM density and relative proximity, is expected to be the brightest source of gamma rays from DM annihilation or decay. Despite a strong background of astrophysical gamma-ray sources, the GC remains one of the best targets to search for a DM signal. This research internship project focuses on developing and applying advanced statistical methods to detect a DM-induced gamma-ray signal from the GC region using data from current gamma-ray observatories. We will implement a multi-dimensional template analysis, incorporating machine learning techniques, to disentangle the overlapping gamma-ray sources and diffuse backgrounds in this complex region. During the 12-month internship at the Max Planck Institute for Nuclear Physics (MPIK), we will have privileged access to real observational data from the H.E.S.S. imaging atmospheric Cherenkov telescope and the HAWC water Cherenkov detector. We will optimize the DM search analysis using these datasets, aiming to either detect a faint DM contribution or, in its absence, to derive stringent constraints on the DM particle's annihilation cross section $\langle\sigma v\rangle$ and mass $m_{\mathrm{DM}}$. The expertise available at MPIK, combined with these unique data, will allow us to validate our analysis framework and improve its sensitivity. The outcome will not only directly address the DM search in current data, but also prepare the groundwork for next-generation experiments like the Cherenkov Telescope Array (CTA) and the Southern Wide-field Gamma-ray Observatory (SWGO). Results will be published and disseminated to the scientific community, contributing to our understanding of both dark matter and high-energy astrophysics. (AU)