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The spectral fitting technique and its ingredients

Abstract

The integrated spectra of stellar systems contain an abundance of information, and its analysis can reveal fundamental parameters such as metallicity, age and star formation history. A method widely used to analyze these spectra is the spectral fitting technique, based on modeling the galaxy spectra with a combination of stellar population spectra from a given library. Despite being a powerful tool for the interpretation of spectra, many ingredients are used in the synthesis of the stellar population models, each with their assumptions and uncertainties. In particular, the libraries of stellar spectra are crucial ingredients. Theoretical stellar libraries are particularly interesting for stellar population models, since it is impossible to reproduce the spectrum of galaxies with chemical histories that differ from our solar neighborhood using only the spectra of observed stars. In this project we propose to explore the uncertainties and limitations of the spectral fitting technique, as it is available today, and, in parallel, to improve the quality of ingredients used by it. To accomplish that we invest in the development of new, more complete empirical libraries, that will help in the building of new stellar population models and to calibrate theoretical stellar spectra, and propose an innovative method to calibrate atomic lines lists - ALiCCE (Atomic Lines Calibration using Cross-Entropy algorithm). This method allows the calibration of atomic parameters to be done in a robust and efficient way. The improved line list can then be used to generate theoretical stellar spectra with unprecedented accuracy. (AU)

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