Profound: Source extraction and application to modern survey data

A. S.G. Robotham, L. J.M. Davies, S. P. Driver, S. Koushan, D. S. Taranu, S. Casura, J. Liske

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

We introduce PROFOUND, a source finding and image analysis package. PROFOUND provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size, and ellipticity. These inputs are key requirements of PROFIT, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in PROFOUND is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than usingmore traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply PROFOUND in a number of simulated and real-world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for PROFIT, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. Anumber of bulge-disc decomposition projects are already making use of the PROFOUND and PROFIT pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

Original languageEnglish
Pages (from-to)3137-3159
Number of pages23
JournalMonthly Notices of the Royal Astronomical Society
Volume476
Issue number3
DOIs
Publication statusPublished - 21 May 2018

Fingerprint

segmentation
photometry
software
ellipse
galaxies
computer programs
image analysis
releasing
ellipses
ellipticity
saddle points
pixel
decomposition
geometry
platforms
pixels
statistics
requirements
profiles

Cite this

@article{b43c1e564d7045d8bec37bcae1018c61,
title = "Profound: Source extraction and application to modern survey data",
abstract = "We introduce PROFOUND, a source finding and image analysis package. PROFOUND provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size, and ellipticity. These inputs are key requirements of PROFIT, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in PROFOUND is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than usingmore traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply PROFOUND in a number of simulated and real-world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for PROFIT, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. Anumber of bulge-disc decomposition projects are already making use of the PROFOUND and PROFIT pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).",
keywords = "Methods: data analysis, Techniques: image processing, Techniques: photometric",
author = "Robotham, {A. S.G.} and Davies, {L. J.M.} and Driver, {S. P.} and S. Koushan and Taranu, {D. S.} and S. Casura and J. Liske",
year = "2018",
month = "5",
day = "21",
doi = "10.1093/mnras/sty440",
language = "English",
volume = "476",
pages = "3137--3159",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "OXFORD UNIV PRESS UNITED KINGDOM",
number = "3",

}

Profound : Source extraction and application to modern survey data. / Robotham, A. S.G.; Davies, L. J.M.; Driver, S. P.; Koushan, S.; Taranu, D. S.; Casura, S.; Liske, J.

In: Monthly Notices of the Royal Astronomical Society, Vol. 476, No. 3, 21.05.2018, p. 3137-3159.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Profound

T2 - Source extraction and application to modern survey data

AU - Robotham, A. S.G.

AU - Davies, L. J.M.

AU - Driver, S. P.

AU - Koushan, S.

AU - Taranu, D. S.

AU - Casura, S.

AU - Liske, J.

PY - 2018/5/21

Y1 - 2018/5/21

N2 - We introduce PROFOUND, a source finding and image analysis package. PROFOUND provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size, and ellipticity. These inputs are key requirements of PROFIT, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in PROFOUND is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than usingmore traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply PROFOUND in a number of simulated and real-world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for PROFIT, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. Anumber of bulge-disc decomposition projects are already making use of the PROFOUND and PROFIT pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

AB - We introduce PROFOUND, a source finding and image analysis package. PROFOUND provides methods to detect sources in noisy images, generate segmentation maps identifying the pixels belonging to each source, and measure statistics like flux, size, and ellipticity. These inputs are key requirements of PROFIT, our recently released galaxy profiling package, where the design aim is that these two software packages will be used in unison to semi-automatically profile large samples of galaxies. The key novel feature introduced in PROFOUND is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux, rather than usingmore traditional circular or ellipse-based photometry. Also, to be less sensitive to pathological segmentation issues, the de-blending is made across saddle points in flux. We apply PROFOUND in a number of simulated and real-world cases, and demonstrate that it behaves reasonably given its stated design goals. In particular, it offers good initial parameter estimation for PROFIT, and also segmentation maps that follow the sometimes complex geometry of resolved sources, whilst capturing nearly all of the flux. Anumber of bulge-disc decomposition projects are already making use of the PROFOUND and PROFIT pipeline, and adoption is being encouraged by publicly releasing the software for the open source R data analysis platform under an LGPL-3 license on GitHub (github.com/asgr/ProFound).

KW - Methods: data analysis

KW - Techniques: image processing

KW - Techniques: photometric

UR - http://www.scopus.com/inward/record.url?scp=85051851898&partnerID=8YFLogxK

U2 - 10.1093/mnras/sty440

DO - 10.1093/mnras/sty440

M3 - Article

VL - 476

SP - 3137

EP - 3159

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 3

ER -