An Active Instance-based Machine Learning method for Stellar Population Studies

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 machine learning algorithm. We tested the method with the retrieval of the star-formation history and dust content in "synthetic" galaxies with a wide range of S/N ratios. The "synthetic" galaxies where constructed using two different grids of high resolution theoretical population synthesis models. The results of our controlled experiment shows that our method can estimate with good speed and accuracy the parameters of the stellar populations that make up the galaxy even for very low S/N input. For a spectrum with S/N=5 the typical average deviation between the input and fitted spectrum is less than 10**{-5}. Additional improvements are achieved using prior knowledge.Comment: 14 pages, 25 figures, accepted by Monthly Notice

Determination of Orbital Parameters of Interacting Galaxies Using Evolution Strategies

Abstract. In this work we apply Evolution Strategies as a method to find the orbital parameters of a pair of interacting galaxies using a single photometric image. Finding the orbital parameters that best match the image is done by posing it as an optimization problem and solving it using Evolution Strategies. Orbital parameters are estimated using position data from the image only, but in some cases is possible to use velocity data. As working directly with galaxies is unfeasible, we have used single simulations for modeling the system of galaxies, we present experimental results using synthetic data instead of a real image, showing that Evolution Strategies can determine orbital parameters of interacting galaxies very accurately

Determination of Initial Conditions of M81 Triplet Using Evolution Strategies

Abstract. In this work we present Evolution Strategies (ES) as an efficient method to approximate the initial conditions of the main interacting group of three galaxies in M81. The M81 group is one of the nearest groups of galaxies. Its biggest galaxy, M81, sits at the core of the group together with its two companions M82 and NGC3077. The interaction between these three galaxies is very well defined on an image taken in HI. In this first attempt we use nonself-gravitating simulations to approximate the initial conditions; even with that restriction our method reproduces the density distribution of the three galaxies with great precision. Results presented here show that ES is an ideally suited method to work in optimization problems in Astrophysics, where the solution is hard to find by common methods. In particular we argue that ES is a good method to find initial conditions of groups of interacting galaxies, where a large number of parameters need to be determined