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The ESA satellite mission Gaia will acquire spectrophotometric observations of several million unresolved galaxies during its five years of operation. In order to implement a classification scheme for these observations we need to build a new library of galaxy spectra which covers the necessary parameter space. Using the evolutionary code PEGASE.2 we have produced a library of 28885 synthetic galaxy spectra at zero redshift covering four general spectral types of galaxies over the wavelength range from 250 to 1050 nm, at a sampling of 1 nm or less. The library was also reproduced for four random values of redshift in the range of 0–0.2 and it is computed on a random grid of four key astrophysical parameters (3 for SFR and 1 for timescale of the infall of gas). The synthetic library was compared with various photometric and spectroscopic observations (e.g. from SDSS) and found in good agreement with them.

The ESA satellite mission Gaia will acquire spectrophotometric observations of several million unresolved galaxies during its five years of operation. Our objective is to design and implement a classification system for these data. For this purpose we need to build a new library of galaxy spectra which covers the necessary parameter space. Using the evolutionary code PEGASE.2 we have produced a library of 28885 synthetic galaxy spectra at zero redshift covering four general spectral types of galaxies over the wavelength range from 250 to 1050 nm, at a sampling of 1 nm or less. The library was also reproduced for 4 random values of redshift in the range of 0–0.2 and it is computed on a random grid of four key astrophysical parameters (3 for SFR and 1 for timescale of the infall of gas). The synthetic library was compared with various photometric and spectroscopic observations (e.g. from SDSS) and found in good agreement with them. Using simulated Gaia photometry of this library we train and test the performance of Support Vector Machine (SVM) classifiers and parametrizers. The first results are promising, indicating that galaxy types can be reliably predicted and several parameters (e.g. redshift, mass to light ratio, present SFR) can be estimated with low bias and variance from Gaia observations.