2016
DOI: 10.1093/mnras/stw3039
|View full text |Cite
|
Sign up to set email alerts
|

ProFit: Bayesian profile fitting of galaxy images

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. lib… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

2
150
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
10

Relationship

1
9

Authors

Journals

citations
Cited by 124 publications
(152 citation statements)
references
References 69 publications
2
150
0
Order By: Relevance
“…This catalogue aimed to robustly measure the morphological parameters of the bulges and discs of galaxies with a low level of spurious fits. We improved on previous bulge-disc decomposition catalogues with the combined usage of ProFit (Robotham et al 2017) -a Bayesian twodimensional galaxy photometric profile fitting code -with additional model filtering and constrained remodelling. Importantly, each galaxy was individually verified and wherever a poor fit was attained (e.g.…”
Section: Sample and Summary Of Datamentioning
confidence: 99%
“…This catalogue aimed to robustly measure the morphological parameters of the bulges and discs of galaxies with a low level of spurious fits. We improved on previous bulge-disc decomposition catalogues with the combined usage of ProFit (Robotham et al 2017) -a Bayesian twodimensional galaxy photometric profile fitting code -with additional model filtering and constrained remodelling. Importantly, each galaxy was individually verified and wherever a poor fit was attained (e.g.…”
Section: Sample and Summary Of Datamentioning
confidence: 99%
“…Lastly, to isolate disk-dominated galaxies we take advantage of the bulge-to-disk decomposition of xGASS galaxies recently presented by Cook et al (2019). This has been derived from g, r and i SDSS imaging data using the Bayesian code ProFIT (Robotham et al 2017). Stellar mass bulge-to-total (B/T) ratios are then determined from the rband profile and the g − i color gradients using the empirical recipes presented in Zibetti et al (2009).…”
Section: Datamentioning
confidence: 99%
“…In this section, we describe our usage of the two-dimensional structural decomposition code ProFit 1 (Robotham et al 2017) that has been designed to robustly decompose galaxy images into their separate bulge and disc components.…”
Section: Structural Decomposition Of Galaxiesmentioning
confidence: 99%