Spatial field reconstruction with INLA: Application to IFU galaxy data
  Astronomical observations of extended sources, such as cubes of integral
field spectroscopy (IFS), encode auto-correlated spatial structures that cannot
be optimally exploited by standard methodologies. This work introduces a novel
technique to model IFS datasets, which treats the observed galaxy properties as
realizations of an unobserved Gaussian Markov random field. The method is
computationally efficient, resilient to the presence of low-signal-to-noise
regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian
inference, the Integrated Nested Laplace Approximation (INLA). As a case study,
we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO
surveys, for which we retrieve the maps of the following physical properties:
age, metallicity, mass and extinction. The proposed Bayesian approach, built on
a generative representation of the galaxy properties, enables the creation of
synthetic images, recovery of areas with bad pixels, and an increased power to
detect structures in datasets subject to substantial noise and/or sparsity of
sampling. A snippet code to reproduce the analysis of this paper is available
in the COIN toolbox, together with the field reconstructions of the CALIFA and
PISCO samples.
