Removing biases in resolved stellar mass-maps of galaxy disks through
  successive Bayesian marginalization
  Stellar masses of galaxies are frequently obtained by fitting stellar
population synthesis models to galaxy photometry or spectra. The state of the
art method resolves spatial structures within a galaxy to assess the total
stellar mass content. In comparison to unresolved studies, resolved methods
yield, on average, higher fractions of stellar mass for galaxies. In this work
we improve the current method in order to mitigate a bias related to the
resolved spatial distribution derived for the mass. The bias consists in an
apparent filamentary mass distribution, and a spatial coincidence between mass
structures and dust lanes near spiral arms. The improved method is based on
iterative Bayesian marginalization, through a new algorithm we have named
Bayesian Successive Priors (BSP). We have applied BSP to M 51, and to a pilot
sample of 90 spiral galaxies from the Ohio State University Bright Spiral
Galaxy Survey. By comparing quantitatively both methods, we find that the
average fraction of stellar mass missed by unresolved studies is only half than
previously thought. In contrast with the previous method, the output BSP
mass-maps bear a better resemblance to near infrared images.
