Linking galaxies and dark matter halos

• Grant number: 24/05768-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: November 01, 2024
• End date: February 28, 2026
• Field of knowledge: Physical Sciences and Mathematics - Astronomy - Extragalactic Astrophysics
• Principal Investigator: Luis Raul Weber Abramo
• Grantee: Daniel López Cano
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 19/26492-3 - Science with the Brazilian robotic telescope, AP.ESP

Abstract

With the new generation of massive astronomical experiments, analyzing the observations will require new and efficient computational techniques. The candidate's main research interests lie in large-scale structure and computational methods in cosmology. During his Ph.D., he acquired expertise in several sopisticated computational tools, especially using Machine Learning techniques, applied to address different problems in astrophysics and cosmology. In this fellowship project, we aim at applying AI techniques to support the processing and interpretation of data from large surveys, namely the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) and the WHT Enhanced Area Velocity Explorer (WEAVE) survey. Techniques developed for the narrow-band data from J-PAS can also be applied to the Southern Photometric Local Universe Survey (S-PLUS), The project can be divided into three main parts: 1) Assessing the quality of massive spectroscopic surveys, 2) predicting of physical features from massive surveys, and 3) characterizing dark matter from spatial galaxy distribution through the galaxy-halo connection. A common thread that ties these projects together is the exploration and study of the Large Scale Structure (LSS) of the Universe. The analysis and predictions carried out in these projects aim to unravel the intricate patterns, distributions, and properties of galaxies and their connection to the underlying cosmic web. By examining the LSS, we gain valuable insights into the formation, evolution, and clustering of galaxies and their relationship with Dark Matter.