Publication Details

AFRICAN RESEARCH NEXUS

SHINING A SPOTLIGHT ON AFRICAN RESEARCH

earth and planetary sciences

P-MaNGA: Full spectral fitting and stellar population maps from prototype observations

Monthly Notices of the Royal Astronomical Society, Volume 449, No. 1, Year 2015

MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-yr SDSS-IV (Sloan Digital Sky Survey IV) survey that will obtain resolved spectroscopy from 3600 to 10 300Å for a representative sample of over 10 000 nearby galaxies. In this paper, we derive spatially resolved stellar population properties and radial gradients by performing full spectral fitting of observed galaxy spectra from P-MaNGA, a prototype of the MaNGA instrument. These data include spectra for 18 galaxies, covering a large range of morphological type. We derive age, metallicity, dust, and stellar mass maps, and their radial gradients, using high spectralresolution stellar population models, and assess the impact of varying the stellar library input to themodels. We introduce amethod to determine dust extinction which is able to give smooth stellar mass maps even in cases of high and spatially non-uniform dust attenuation. With the spectral fitting, we produce detailed maps of stellar population properties which allow us to identify galactic features among this diverse sample such as spiral structure, smooth radial profiles with little azimuthal structure in spheroidal galaxies, and spatially distinct galaxy sub-components. In agreement with the literature, we find the gradients for galaxies identified as early type to be on average flat in age, and negative (-0.15 dex/Re) in metallicity, whereas the gradients for late-type galaxies are on average negative in age (-0.39 dex/Re) and flat in metallicity. We demonstrate how different levels of data quality change the precision with which radial gradients can be measured. We show how this analysis, extended to the large numbers of MaNGA galaxies, will have the potential to shed light on galaxy structure and evolution. © 2015 The Authors.

Statistics
Citations: 76
Authors: 34
Affiliations: 17
Identifiers
Study Design
Cross Sectional Study
Study Approach
Quantitative