paper

ProFit: Bayesian Profile Fitting of Galaxy Images

arXiv:1611.08586 · doi:10.1093/mnras/stw3039

Abstract

We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available 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 Serrsic profile for the most common values of the Serrsic 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-fit parameters from fitting images from different sources (SDSS vs KiDS) than from using different codes (ProFit vs 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.

32 pages, 34 Figures, accepted for publication in MNRAS

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