Machine learning techniques applied to cosmological problems

• Grant number: 19/08852-2
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: June 01, 2019
• End date: May 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Physics - Elementary Particle Physics and Fields
• Principal Investigator: Nathan Jacob Berkovits
• Grantee: Martín Emilio de los Rios
• Host Institution: Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
• Associated research grant: 16/01343-7 - ICTP South American Institute for Fundamental Research: a regional center for theoretical physics, AP.ESP

Abstract

Machine learning techniques represents a new way of analysing big datasets in an agnostic and homogeneous way. These methods are very useful and powerful tolls to find patterns and relations between the variables that are involved in an specific problem. It is worth to mention that these techniques have been applied with a lot of success in technological problems and in other different areas of science, including astronomy and physics. On the other hand, current and future astronomic surveys will generate enormous amount of information, making the machine learning techniques important tools for their analysis. During this postdoc I will applied this new techniques to different cosmological problems. Specifically I will improve the machine learning algorithms for the automatic classification of merging clusters, in order to applied it for high-redshift and Sunyaev-Zeldovich clusters. At the same time I will continue studying in an individual way the merging clusters candidates found previously. I also will continue the studies of the cosmic microwave background anisotropies with the aim of find if there are any signal of the departure from the standard cosmological model. Specifically I will apply anomaly detection algorithms to find zones of the sky which may present deviations from what it is expected. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE LOS RIOS, MARTIN. COSMIC-KITE: auto-encoding the cosmic microwave background. Monthly Notices of the Royal Astronomical Society, v. 511, n. 4, p. 11-pg., 2022-03-08. (19/08852-2)

DE LOS RIOS, MARTIN; PETAC, MIHAEL; ZALDIVAR, BRYAN; BONAVENTURA, NINA R.; CALORE, FRANCESCA; IOCCO, FABIO. Determining the dark matter distribution in simulated galaxies with deep learning. Monthly Notices of the Royal Astronomical Society, v. 525, n. 4, p. 21-pg., 2023-09-11. (19/08852-2)

COENDA, VALERIA; DE LOS RIOS, MARTIN; MURIEL, HERNAN; CORA, SOFIA A.; MARTINEZ, HECTOR J.; RUIZ, ANDRES N.; VEGA-MARTINEZ, CRISTIAN A.. econstructing orbits of galaxies in extreme regions (ROGER) - II: reliability of projected phase-space in our understanding of galaxy population. Monthly Notices of the Royal Astronomical Society, v. 510, n. 2, p. 1934-1944, FEB 2022. (19/08852-2)