This value added catalog provides resolved and integrated (aperture-corrected) stellar masses for galaxies in MaNGA DR17. The resolved masses are found by adopting a reduced spectral-fitting space derived from principal component analysis of 40000 synthetic spectra of continuous stellar populations, and using the goodness-of-fit of each synthetic spectrum for a given observed spectrum, to define a marginalized posterior probability density function for the i-band stellar mass-to-light ratio. The median of that distribution is adopted as the fiducial stellar mass-to-light ratio of a spaxel (line of sight in a galaxy), and multiplied by the i-band luminosity to get an estimate for the stellar mass. Stellar mass-to-light ratios have been vetted against synthetic spectra, and found to be reliable at median signal-to-noise ratios between 2 and 20, across a wide range of dust attenuation conditions, and across the full range of realistic stellar metallicities. Typical 'random' uncertainties are approximately 0.1 dex (including age-metallicity degeneracies and uncertainties induced by imperfect spectrophotometry), and systematic uncertainties could be as high as 0.3 dex, but we believe are more realistically 0.1 to 0.15 dex (see Pace et al. 2019). We include several data-quality metrics, to help the user decide which data are reliable for their purposes.
In addition to resolved maps of stellar mass-to-light ratio and i-band luminosity, we will soon release a full catalog of total stellar-masses for DR17. One column gives the total mass inside the IFU (after interpolating over foreground stars and unreliable measurements). We also supply two possible masses intended to correct for mass falling outside the spatial grasp of the IFU: the first adopts the median stellar mass-to-light ratio of the outermost 0.5 effective radii, and the second adopts a mass-to-light ratio consistent with the (g - r) color of the NASA-Sloan Atlas (NSA) flux minus the flux in the IFU (see Pace et al. 2019b,).
The python code used to generate this VAC can be found on Github. Lightweight python routines allowing access to the data products can be found in read_results.py.
We recommend that you always use the latest version of this catalog, which is DR17. The DR15 version of this VAC is still available on the SAS here, with datamodels available here.
