Bayesian bulge-disc decomposition of galaxy images
  We introduce PHI, a fully Bayesian Markov-chain Monte Carlo algorithm
designed for the structural decomposition of galaxy images. PHI uses a triple
layer approach to effectively and efficiently explore the complex parameter
space. Combining this with the use of priors to prevent nonphysical models, PHI
offers a number of significant advantages for estimating surface brightness
profile parameters over traditional optimisation algorithms. We apply PHI to a
sample of synthetic galaxies with SDSS-like image properties to investigate the
effect of galaxy properties on our ability to recover unbiased and well
constrained structural parameters. In two-component bulge+disc galaxies we find
that the bulge structural parameters are recovered less well than those of the
disc, particularly when the bulge contributes a lower fraction to the
luminosity, or is barely resolved with respect to the pixel scale or PSF. There
are few systematic biases, apart from for bulge+disc galaxies with large bulge
Sersic parameter, n. On application to SDSS images, we find good agreement with
other codes, when run on the same images with the same masks, weights, and PSF.
Again, we find that bulge parameters are the most difficult to constrain
robustly. Finally, we explore the use of a Bayesian Information Criterion (BIC)
method for deciding whether a galaxy has one- or two-components.
