Precise chemical abundances for solar twins using Machine Learning

• Grant number: 23/15044-5
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: March 01, 2024
• End date: August 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Astronomy - Stellar Astrophysics
• Principal Investigator: Jorge Luis Melendez Moreno
• Grantee: Giulia Martos
• Supervisor: Sara Lucatello
• Host Institution: Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Osservatorio Astronomico Di Padova, Italy
• Associated to the scholarship: 22/05833-0 - Planetary chemical signatures in the spectrum of solar twin stars, BP.MS

Abstract

Solar twins were important to develop a manual method of precise differential chemical abundances, allowing to achieve a precision at the 0.01 - 0.02 dex level. In this project we aim to use solar twins to develop an automatic code to achieve the same level of precision that is achieved with the manual line-by-line differential method, or perhaps even better, at the 0.01 dex or sub-0.01 dex precision, by applying machine learning algorithms to a sample of 91 solar twins. With this precision, it is possible to go beyond current global analyses of stellar samples, and perform chemical characterization of each individual star, allowing to better separate the signature of planets from the chemical evolution of the Galaxy. Besides, we will benefit from developing an automated method to determine differential abundances with a precision at the 0.01 dex level, that currently can only be obtained through time-consuming line-by-line hand measurements.