Automated Determination of Stellar Population Parameters in Galaxies Using Active Instance-based Learning

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dc.creatorSolorio, Thamar
dc.creatorFuentes, Olac
dc.creatorTerlevich, Roberto
dc.creatorTerlevich, Elena
dc.creatorBressan, Sandro
dc.date2003-12-02
dc.date.accessioned2026-07-07T02:11:25Z
dc.date.available2026-07-07T02:11:25Z
dc.descriptionIn 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.
dc.description4 pages, 1 figure, To appear in Proceedings of ADASS XIII, Strasbourg, October 2003
dc.identifierhttps://arxiv.org/abs/astro-ph/0312073
dc.identifierhttp://arxiv.org/abs/astro-ph/0312073
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/7076
dc.subjectAstrophysics
dc.titleAutomated Determination of Stellar Population Parameters in Galaxies Using Active Instance-based Learning
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