Galaxy rotation curve fitting using state-of-the-art machine learning tools

SANTIAGO COLLAZO; CARLOS ARGÜELLES; MARTÍN MESTRE

Córdoba

Congreso; Friend of friends meeting; 2022

Instituto de Astronomía Teórica y Experimental - Observatorio Astronómico de Córdoba

Nowadays machine learning is a tremendously powerful tool to solve a lot of different problems. In this work, we will use a specific machine learning tool known as gradient descent to fit the observed Galaxy’s rotation curve. We will perform this fitting by assuming a theoretical velocity profile, arising from a composite model which includes baryons and a fermionic dark matter component. The last one explains the Galactic halo through a semi-analytical model of self-gravitating quantum fermions under the frame of general relativity. It has four free parameters including the particle mass which, in addition to the free parameters of the baryons, will be constrained by minimizing a loss function through the aforementioned gradient descent method.