Predicting Maximum Circular Velocity in a sample of MaNGA galaxies using artificial intelligence

Octavio Valenzuela, Erick Aquino, Arturo Trelles

TL;DR: Artificial Intelligence apply to predict galaxy properties.

Keywords: Galaxy, prediction, catalog, sample, deep learning, machine learning

Abstract: We use artificial intelligence algorithms to predict the Vmax (Maximum circular velocity) value in a galaxy sample of 200 resolved MaNGA and CALIFA galaxies with velocity fields, using different properties, like stellar mass, circular velocity at effective radius and star formation rate to build the training sample. Here we present the predicted results from apply a Random Forest algorithm and a Cross validated artificial neural network to our sample.