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Paper: Automated Determination of Stellar Population Parameters in Galaxies Using Active Instance-based Learning
Volume: 314, Astronomical Data Analysis Software and Systems XIII
Page: 609
Authors: Solorio, T.; Fuentes, O.; Terlevich, R.; Terlevich, E.; Bressan, A.
Abstract: 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.
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