Spatially resolved mass-to-light from the CALIFA survey. Mass-to-light ratio vs. color relations
arXiv:1811.08431 · doi:10.1051/0004-6361/201833993
Abstract
We investigated the mass-to-light versus color relations (MLCRs) derived from the spatially resolved star formation history of a sample of 452 galaxies observed with integral field spectroscopy in the CALIFA survey. We derived the stellar mass () and the stellar mass surface density from the combination of full spectral fitting (using different sets of stellar population models) with observed and synthetic colors in optical broad bands. This method allows obtaining the radial structure of the mass-to-light ratio () at several wavelengths and studying the spatially resolved MLCRs. Our sample covers a wide range of Hubble types from Sc to E, with stellar masses ranging from to M. The scatter in the MLCRs was studied as a function of morphology, stellar extinction, and emission line contribution to the colors. The effects of the initial mass function (IMF) and stellar population models in the MLCRs were also explored. Our main results are that (a) the ratio has a negative radial gradient that is steeper within the central 1 half-light-radius (HLR). It is steeper in Sb-Sbc than in early-type galaxies. (b) The MLCRs between and optical colors were derived with a scatter of 0.1 dex. Extinction and emission line contributions do not affect the scatter of these relations. Morphology does not produce a significant effect, except if the general relation is used for galaxies redder than or bluer than . (c) The IMF has a large effect on MLCRs, as expected. The change from a Chabrier to a Salpeter IMF produces a median shift of \oimf\ dex when mass loss from stellar evolution is also taken into account. (d) These MLCRs are in agreement with previous results, in particular for relations with and bands and the and Johnson systems. FITS tables available at http://pycasso.iaa.es/ML
arXiv comment13 pages, 10 figures, 12 tables, accepted for publication in Astronomy & Astrophysics. Abridged abstract. All tables are available in electronic format at http://pycasso.iaa.es/ML
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