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dc.contributor.authorRobotham, A. S. G.
dc.contributor.authorTaranu, D. S.
dc.contributor.authorTobar, R.
dc.contributor.authorMoffett, A.
dc.contributor.authorDriver, S. P.
dc.date.accessioned2018-01-22T15:30:34Z
dc.date.available2018-01-22T15:30:34Z
dc.date.issued2017-04
dc.identifier.citationRobotham , A S G , Taranu , D S , Tobar , R , Moffett , A & Driver , S P 2017 , ' ProFit : Bayesian profile fitting of galaxy images ' , Monthly Notices of the Royal Astronomical Society , vol. 466 , no. 2 , pp. 1513-1541 . https://doi.org/10.1093/mnras/stw3039en
dc.identifier.issn0035-8711
dc.identifier.otherPURE: 252098030
dc.identifier.otherPURE UUID: 75b7e8fa-12cc-4463-a5c8-7896b1ed7a7d
dc.identifier.otherArXiv: http://arxiv.org/abs/1611.08586v2
dc.identifier.otherScopus: 85037619720
dc.identifier.urihttp://hdl.handle.net/10023/12574
dc.description.abstractWe 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 Sérsic, Core-Sérsic, 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 Sérsic profile for the most common values of the Sérsic 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.
dc.language.isoeng
dc.relation.ispartofMonthly Notices of the Royal Astronomical Societyen
dc.rights© 2016, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at https://doi.org/10.1093/mnras/stw3039en
dc.subjectMethods: data analysisen
dc.subjectMethods: statisticalen
dc.subjectTechniques: photometricen
dc.subjectGalaxies: fundamental parametersen
dc.subjectGalaxiess: statisticsen
dc.subjectGalaxies: structureen
dc.subjectQB Astronomyen
dc.subjectQC Physicsen
dc.subjectDASen
dc.subject.lccQBen
dc.subject.lccQCen
dc.titleProFit : Bayesian profile fitting of galaxy imagesen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.1093/mnras/stw3039
dc.description.statusPeer revieweden
dc.identifier.urlhttp://arxiv.org/abs/1611.08586v2en


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