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

• Grant number: 23/05082-7
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2025
• End date: January 31, 2028
• Field of knowledge: Physical Sciences and Mathematics - Astronomy - Extragalactic Astrophysics
• Principal Investigator: Luis Raul Weber Abramo
• Grantee: Luis Raul Weber Abramo
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Antonio David Montero Dorta
• Associated research grant: 22/03426-8 - Exploiting two large astrophysical surveys: WEAVE-QSO and J-PAS, AP.R

Abstract

Cosmological applications of galaxy surveys depend on the proper identification of sources and extraction of the redshifts of extragalactic objects. This can be greatly enhanced by combining data from narrow-band surveys such as S-PLUS, J-PAS and J-PLUS with the deep photometry, superior spatial resolution and time domain capabilities of the Vera Rubin LSST.We will employ Machine Learning techniques in order to combine pseudo-spectral data from these optical surveys with photometric, variability and morphology data from the LSST, with the goal of using them for cosmological applications.We will also study the combination of LSST data with different tracers (galaxies, quasars, intensity mapping) and other messengers (such as dark sirens). These multiple tracers will serve two goals: on one hand, we want to study large-scale effects that can only be measured on extremely large (angular or radial) distances. On the other hand, we will use these multiple tracers in order to better understand the connection between them and the halos they inhabit -- the so-called galaxy-halo connection. (AU)