ProFit: Bayesian profile fitting of galaxy images

pre-built from CRAN; however, this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two-dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source, and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Sersic profile for the most common values of the Sersic index n (0.5 <n <8). The high-level fitting code PROFIT is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fitting parameters from fitting images from different sources (SDSS versus KiDS) than from using different codes (PROFIT versus GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with PROFIT on SDSS, KiDS, and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS-to KiDS-quality data, with less significant gains moving from KiDS to LSST.