Modeling low-resolution galaxy spectral energy distribution with evolutionary algorithms

Abstract Astrophysics and Cosmology are entering in a new epoch in which an extremely large volume of data is accessible by the researchers. Consequently, the researchers have to modify their procedures for data analysis to adapt them to this new scenario. This requires the incorporation of new scientific computing resources able to maintain, at least, the present scientific production performance. As part of this increment in the data volume, it should be underlined the number of high-quality galaxy spectra produced by the new instruments. Galaxy spectra are important in Astrophysics because of they encode essential information, such as age and metallicity, of the constituent stellar populations, and, therefore the evolutionary history of the corresponding galaxy. In this work, these galaxy spectra are modeled by using Simple Stellar Populations. This mechanism to model the galaxy spectra allows an in-depth understanding of the present state of the galaxy, but also it allows understanding its past evolution. However, this modeling requires to adequately combine more than one Simple Stellar Population to reproduce the galaxy spectral energy distribution. To find high-quality solutions, metaheuristic algorithms are suitable. In this work, a wide portfolio of metaheuristics are evaluated to reproduce the Low-Resolution Spectra Energy Distribution of galaxies: M110, M32, and NGC3190. The final aim of this work is to advance in the evaluation of some metaheuristics for modeling the Spectral Energy Distribution of a galaxy as a combination of predefined Simple Stellar Populations Spectra.

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