Classification of the nuclear emission of galaxies with machine learning applied to spectroscopic data

• Grant number: 24/13784-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2024
• End date: October 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Astronomy - Extragalactic Astrophysics
• Principal Investigator: Roberto Bertoldo Menezes
• Grantee: Arthur Gama Ruiz
• Host Institution: Escola de Engenharia Mauá (EEM). Instituto Mauá de Tecnologia. São Caetano do Sul , SP, Brazil

Abstract

Galactic nuclei have a considerable importance, as they can provide information about the formation and evolution of galaxies. In that context, the classification of the nuclear emission of galaxies, which can be due to the stars there or to the accretion of matter into a central supermassive black hole, is one of the most relevant analyses. Currently, this classification is done, based on spectroscopic data, using diagnostic diagrams, which are graphs relating two emission-line ratios. Although very useful, this method may result in inaccurate or dubious classifications. In this work, we intend to develop a methodology, involving machine learning techniques, to be applied to spectroscopic data, in order to improve the precision of the classification of the nuclear emission of galaxies. Due to the relevance of this topic to Extragalactic Astrophysics, this project will probably result in a publication, with significant impact.