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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.
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an instance-based
We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained
We present single stellar population (SSP) equivalent ages, metallicities, and abundance ratios for local elliptical galaxies derived from Hbeta, Mgb, and <Fe> absorption line strengths. We use an extension of the Worthey (1994) stellar population mo
We analyze single-stellar-population (SSP) equivalent parameters for 50 local elliptical galaxies as a function of their structural parameters. These galaxies fill a two-dimensional plane in the four-dimensional space of [Z/H], log t, log $sigma$, an
To understand the physical origin of the close connection between supermassive black holes and their host galaxies, it is vital to investigate star formation properties in active galaxies. Using a large dataset of nearby type 1 active galactic nuclei