Automated Determination of Stellar Population Parameters in Galaxies Using Active Instance-based Learning
Thamar Solorio, Olac Fuentes, Roberto J. Terlevich, E. Terlevich, Sandro Bressan · arXiv (Cornell University) · 2003
In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model galaxies with a distribution of ages, metallicities and intrinsic reddening. Using this set we have explored a new fitting method that presents several advantages over conventional methods. We propose an optimization technique that combines active learning with an instance-based machine learning algorithm. Experimental results show that this method can estimate with high speed and accuracy the physical parameters of the stellar populations.