Galaxy growth in 1 and 2D: 1st results from the CALIFA survey
Roberto Cid Fernandes (UFSC & IAA)
Spectral synthesis of galaxies is the science/art of digging as much information as possible from galaxy spectra. In the last decade, evolutionary synthesis models with decent spectral resolution in the optical have promoted huge advances in this area, which, combined with the avalanche of data has lead to substantial progress in our understanding of galaxies. The STARLIGHT code is one such tool in this business. It performs Angstrom-by-Angstrom fits of galaxy spectra combining models of simple stellar populations of various ages and metallicities. It has been employed to address a variety of issues, from problems where one seeks information on stellar population mixtures (a.k.a. Star Formation Histories), to others where one is more interested in getting rid of stellar light in order to measure emission lines, to study stellar kinematics, and even to test the ingredients (SSP models) themselves.
This talk will (1) briefly review the basics of spectral synthesis and illustrate its power as a tool to study galaxy evolution in 1D (ie, SDSS-like integrated spectra), and (2) move on to the main course: I will present the 1st results of the application of STARLIGHT to the 1st 100 galaxies observed in the CALIFA survey. CALIFA is collecting data cubes (integral field spectroscopy) for 600 galaxies (~ 1000 spectra per target) spread over the color magnitude diagram, producing a database quantitatively comparable with (but qualitatively very different from) the SDSS. Besides its own importance, this pioneer IFS survey serves as a test-bed for future projects, like SAMI and MaNGA. We have developed an end-to-end pipeline to processes data cubes through STARLIGHT, thus allowing us to resolve galaxies in time and space. Dissecting galaxy spectra as a function of position is showing us how the different morphological components of galaxies build their mass and metals over time, and promises to provide important clues for galaxy evolution models in general.