paper

Population synthesis modelling of luminous infrared galaxies at intermediate redshift

arXiv:1006.5555 · doi:10.1051/0004-6361/201014898

Abstract

Luminous InfraRed Galaxies (LIRGs) are particularly important for studying the build-up of the stellar mass from z=1 to z=0. We perform a multiwavelength study of an LIRGs sample in the Extended Chandra Deep Field South at z=0.7, selected at 24 μ\m by MIPS onboard Spitzer Space Telescope and detected in 17 filters. Data go from the near-ultraviolet to the mid-infrared. This multiwavelengths dataset allows us to place strong constraints on the spectral energy distributions (SEDs) of galaxies, and thus to efficiently derive physical parameters such as the SFR, the total infrared luminosity, attenuation parameters, and star formation history. An important part of this work is elaboration of a mock catalogue that allows us to have a reliability criterion for the derived parameters. We studied LIRGs by means of an SED-fitting code CIGALE. At first, this code creates synthetic spectra from the Maraston stellar population models. The stellar population spectra are attenuated by using a synthetic Calzetti-based attenuation law before adding the dust emission as given by the infrared SED library. The originality of CIGALE is that it allows us to perform consistent fits of the dust-affected ultaviolet-to-infrared wavelength range. This technique appears to be a very powerful tool in the case where we can have access to a dataset that is well-sampled over a wide range of wavelengths. We are able to derive a star formation history and to estimate the fraction of infrared luminosity reprocessed by an active galactic nucleus. We study the dust temperatures of our galaxies detected at 70 μ\m and find them colder than predicted by models. We also study the relation between the SFR and the stellar mass and do not find a tight correlation between either of them, but instead a flat distribution and a large scatter, which is interpreted in terms of variations in star formation history.

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