Combining LSST and narrow-band surveys using Machine Learning: quasar cosmology, multi-messenger and multi-tracer applications

• Grant number: 25/11898-5
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: October 01, 2025
• End date: September 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Astronomy - Extragalactic Astrophysics
• Principal Investigator: Luis Raul Weber Abramo
• Grantee: Joaquin Andres Armijo Torres
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/05082-7 - Combining LSST and narrow-band surveys using Machine Learning: quasar cosmology and multi-tracer applications, AP.R

Abstract

The research plan focuses on understanding the Universe through observational data and advanced statistical techniques, with a strong emphasis on the Rubin-LSST project. This work aims to address key cosmological questions such as the nature of dark energy and the relationship between dark matter and observed galaxies. The approach proposed here involves applying higher-order statistics (beyond the two-point function), and novel machine learning methods to analyze galaxy distribution, clustering, and shapes. The primary data sets will be the Rubin-LSST by itself, or in combination with other surveys in broad- or narrow-bands, as well as other tracers and messengers (such as gravitational wave observations). In particular, we plan to utilize a simulation-based pipeline to obtain highly accurate constraints on the cosmological model, focusing on analyzing reconstructed fields from Rubin-LSST data, such as number-density and velocity-dependent properties, in order to understand matter distribution under the Lambda-cold-dark matter (¿CDM) model.