Towards a library of synthetic galaxy spectra and preliminary results of classification and parametrization of unresolved galaxies for Gaia

TL;DR

This paper introduces a synthetic galaxy spectra library and demonstrates initial success in classifying galaxy types and estimating parameters from simulated Gaia spectrophotometry using Support Vector Machines.

Contribution

It presents a new synthetic galaxy spectra library and applies SVMs for galaxy classification and parameter estimation from Gaia-like data.

Findings

Support Vector Machines reliably predict galaxy Hubble types.

Synthetic spectra match SDSS data across galaxy types.

Initial classification and parametrization results are promising.

Abstract

Aims:The Gaia astrometric survey mission will, as a consequence of its scanning law, obtain low resolution optical (330-1000 nm) spectrophotometry of several million unresolved galaxies brighter than V=22. We present the first steps in a project to design and implement a classification system for these data. The goal is both to determine morphological classes and to estimate intrinsic astrophysical parameters via synthetic templates. Here we describe (1) a new library of synthetic galaxy spectra, and (2) first results of classification and parametrization experiments using simulated Gaia spectrophotometry of this library. Methods:We have created a large grid of synthetic galaxy spectra using the PEGASE.2 code, which is based on galaxy evolution models that take into account metallicity evolution, extinction correction, emission lines (with stellar spectra based on the BaSeL library). Our classification and regression models are Support Vector Machines (SVMs), which are kernel-based nonlinear estimators. Results:We produce a basic library of about 4000 zero redshift galaxy spectra covering the main Hubble types over wavelength range 250 to 1050 nm at a sampling of 1 nm or less. It is computed on a regular grid of four key astrophysical parameters for each type and for intermediate random values of the same parameters. An extended library reproduces this at a series of redshifts. Initial results from the SVM classifiers and parametrizers are promising, indicating that Hubble types can be reliably predicted and several parameters estimated with low bias and variance. Comparing the colours of our synthetic library with Sloan Digital Sky Survey (SDSS) spectra we find good agreement over the full range of Hubble types and parameters.