A semi-empirical library of galaxy spectra for Gaia classification based on SDSS data and PEGASE models

TL;DR

This paper creates a semi-empirical galaxy spectral library by fitting SDSS data with synthetic models, enabling improved Gaia galaxy classification and parameter estimation using machine learning.

Contribution

It introduces a semi-empirical galaxy spectral library combining observed and synthetic spectra for Gaia data analysis, enhancing classification accuracy.

Findings

The library contains 33,670 spectra covering 250-1050 nm.

Semi-empirical spectra improve Gaia classification performance.

Fitting results help constrain galaxy model parameters.

Abstract

Aims:This paper is the third in a series implementing a classification system for Gaia observations of unresolved galaxies. The system makes use of template galaxy spectra in order to determine spectral classes and estimate intrinsic astrophysical parameters. In previous work we used synthetic galaxy spectra produced by PEGASE.2 code to simulate Gaia observations and to test the performance of Support Vector Machine (SVM) classifiers and parametrizers. Here we produce a semi-empirical library of galaxy spectra by fitting SDSS spectra with the previously produced synthetic libraries. We present (1) the semi-empirical library of galaxy spectra, (2) a comparison between the observed and synthetic spectra, and (3) first results of claassification and parametrization experiments with simulated Gaia spectrophotometry of this library. Methods: We use chi2-fitting to fit SDSS galaxy spectra with the synthetic library in order to construct a semi-empirical library of galaxy spectra in which (1) the real spectra are extended by the synthetic ones in order to cover the full wavelength range of Gaia, and (2) astrophysical parameters are assigned to the SDSS spectra by the best fitting synthetic spectrum. The SVM models were trained with and applied to semi-empirical spectra. Tests were performed for the classification of spectral types and the estimation of the most significant galaxy parameters (in particular redshift, mass to light ratio and star formation history). Results: We produce a semi-empirical library of 33670 galaxy spectra covering the wavelength range 250 to 1050 nm at a sampling of 1 nm or less. Using the results of the fitting of the SDSS spectra with our synthetic library, we investigate the range of the input model parameters that produces spectra which are in good agreement with observations. (abridged)