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Characterization of low surface brightness structures in annotated deep images
Elisabeth Sola ,
Pierre-Alain Duc ,
Felix Richards,
Adeline Paiement ,
Mathias Urbano ,
Julie Klehammer,
Michal Bílek ,
Jean-Charles Cuillandre,
Stephen Gwyn,
Alan McConnachie
Astronomy and Astrophysics, Volume: 662, Start page: A124
Swansea University Author: Felix Richards
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DOI (Published version): 10.1051/0004-6361/202142675
Abstract
Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varyin...
Published in: | Astronomy and Astrophysics |
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ISSN: | 0004-6361 1432-0746 |
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EDP Sciences
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa60544 |
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<?xml version="1.0"?><rfc1807><datestamp>2022-08-19T14:00:43.6084567</datestamp><bib-version>v2</bib-version><id>60544</id><entry>2022-07-20</entry><title>Characterization of low surface brightness structures in annotated deep images</title><swanseaauthors><author><sid>c83cb65598539b67ba65727ebb3a3f28</sid><firstname>Felix</firstname><surname>Richards</surname><name>Felix Richards</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-07-20</date><deptcode>SCS</deptcode><abstract>Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varying sizes, either through visual inspection or more recently with deep learning. Detailed analyses including photometry have been carried out for a small number of objects, essentially because of the lack of convenient tools able to precisely characterize tidal structures around large samples of galaxies.Aims. Our goal is to characterize in detail, and in particular obtain quantitative measurements, of LSB structures identified in deep images of samples consisting of hundreds of galaxies.Methods. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures with precision, as well as artifacts or foreground structures. All parameters are automatically stored in a database which may be queried to retrieve quantitative measurements. We annotated LSB structures around 352 nearby massive galaxies with deep images obtained with the Canada-France-Hawaii Telescope as part of two large programs: Mass Assembly of early-Type GaLAxies with their fine Structures and Ultraviolet Near Infrared Optical Northern Survey/Canada-France Imaging Survey. Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated with a disrupted satellite, tails that formed in major mergers, ghost reflections, or cirrus.Results. From our database containing 8441 annotations, the area, size, median surface brightness, and distance to the host of 228 structures were computed. The results confirm the fact that tidal structures defined as streams are thinner than tails, as expected by numerical simulations. In addition, tidal tails appear to exhibit a higher surface brightness than streams (by about 1 mag), which may be related to different survival times for the two types of collisional debris. We did not detect any tidal feature fainter than 27.5 magarcsec−2, while the nominal surface brightness limits of our surveys range between 28.3 and 29 magarcsec−2, a difference that needs to be taken into account when estimating the sensitivity of future surveys to identify LSB structures.Conclusions. We compiled an annotation database of observed LSB structures around nearby massive galaxies including tidal features that may be used for quantitative analysis and as a training set for machine learning algorithms.</abstract><type>Journal Article</type><journal>Astronomy and Astrophysics</journal><volume>662</volume><journalNumber/><paginationStart>A124</paginationStart><paginationEnd/><publisher>EDP Sciences</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0004-6361</issnPrint><issnElectronic>1432-0746</issnElectronic><keywords/><publishedDay>1</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-07-01</publishedDate><doi>10.1051/0004-6361/202142675</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>F.R. was supported by Science and Technology Facilities Council (STFC) grant ST/P006779/1.</funders><projectreference/><lastEdited>2022-08-19T14:00:43.6084567</lastEdited><Created>2022-07-20T09:48:22.5252115</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Elisabeth</firstname><surname>Sola</surname><orcid>0000-0002-2814-3578</orcid><order>1</order></author><author><firstname>Pierre-Alain</firstname><surname>Duc</surname><orcid>0000-0003-3343-6284</orcid><order>2</order></author><author><firstname>Felix</firstname><surname>Richards</surname><order>3</order></author><author><firstname>Adeline</firstname><surname>Paiement</surname><orcid>0000-0001-5114-1514</orcid><order>4</order></author><author><firstname>Mathias</firstname><surname>Urbano</surname><orcid>0000-0001-5640-0650</orcid><order>5</order></author><author><firstname>Julie</firstname><surname>Klehammer</surname><order>6</order></author><author><firstname>Michal</firstname><surname>Bílek</surname><orcid>0000-0003-3104-3372</orcid><order>7</order></author><author><firstname>Jean-Charles</firstname><surname>Cuillandre</surname><order>8</order></author><author><firstname>Stephen</firstname><surname>Gwyn</surname><order>9</order></author><author><firstname>Alan</firstname><surname>McConnachie</surname><order>10</order></author></authors><documents><document><filename>60544__24969__d7f179d67a3741faba40c60788e16e33.pdf</filename><originalFilename>60544.pdf</originalFilename><uploaded>2022-08-19T14:00:20.7122303</uploaded><type>Output</type><contentLength>23948077</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Released under the terms of the Creative Commons Attribution License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0</licence></document></documents><OutputDurs/></rfc1807> |
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2022-08-19T14:00:43.6084567 v2 60544 2022-07-20 Characterization of low surface brightness structures in annotated deep images c83cb65598539b67ba65727ebb3a3f28 Felix Richards Felix Richards true false 2022-07-20 SCS Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varying sizes, either through visual inspection or more recently with deep learning. Detailed analyses including photometry have been carried out for a small number of objects, essentially because of the lack of convenient tools able to precisely characterize tidal structures around large samples of galaxies.Aims. Our goal is to characterize in detail, and in particular obtain quantitative measurements, of LSB structures identified in deep images of samples consisting of hundreds of galaxies.Methods. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures with precision, as well as artifacts or foreground structures. All parameters are automatically stored in a database which may be queried to retrieve quantitative measurements. We annotated LSB structures around 352 nearby massive galaxies with deep images obtained with the Canada-France-Hawaii Telescope as part of two large programs: Mass Assembly of early-Type GaLAxies with their fine Structures and Ultraviolet Near Infrared Optical Northern Survey/Canada-France Imaging Survey. Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated with a disrupted satellite, tails that formed in major mergers, ghost reflections, or cirrus.Results. From our database containing 8441 annotations, the area, size, median surface brightness, and distance to the host of 228 structures were computed. The results confirm the fact that tidal structures defined as streams are thinner than tails, as expected by numerical simulations. In addition, tidal tails appear to exhibit a higher surface brightness than streams (by about 1 mag), which may be related to different survival times for the two types of collisional debris. We did not detect any tidal feature fainter than 27.5 magarcsec−2, while the nominal surface brightness limits of our surveys range between 28.3 and 29 magarcsec−2, a difference that needs to be taken into account when estimating the sensitivity of future surveys to identify LSB structures.Conclusions. We compiled an annotation database of observed LSB structures around nearby massive galaxies including tidal features that may be used for quantitative analysis and as a training set for machine learning algorithms. Journal Article Astronomy and Astrophysics 662 A124 EDP Sciences 0004-6361 1432-0746 1 7 2022 2022-07-01 10.1051/0004-6361/202142675 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University F.R. was supported by Science and Technology Facilities Council (STFC) grant ST/P006779/1. 2022-08-19T14:00:43.6084567 2022-07-20T09:48:22.5252115 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Elisabeth Sola 0000-0002-2814-3578 1 Pierre-Alain Duc 0000-0003-3343-6284 2 Felix Richards 3 Adeline Paiement 0000-0001-5114-1514 4 Mathias Urbano 0000-0001-5640-0650 5 Julie Klehammer 6 Michal Bílek 0000-0003-3104-3372 7 Jean-Charles Cuillandre 8 Stephen Gwyn 9 Alan McConnachie 10 60544__24969__d7f179d67a3741faba40c60788e16e33.pdf 60544.pdf 2022-08-19T14:00:20.7122303 Output 23948077 application/pdf Version of Record true Released under the terms of the Creative Commons Attribution License true eng https://creativecommons.org/licenses/by/4.0 |
title |
Characterization of low surface brightness structures in annotated deep images |
spellingShingle |
Characterization of low surface brightness structures in annotated deep images Felix Richards |
title_short |
Characterization of low surface brightness structures in annotated deep images |
title_full |
Characterization of low surface brightness structures in annotated deep images |
title_fullStr |
Characterization of low surface brightness structures in annotated deep images |
title_full_unstemmed |
Characterization of low surface brightness structures in annotated deep images |
title_sort |
Characterization of low surface brightness structures in annotated deep images |
author_id_str_mv |
c83cb65598539b67ba65727ebb3a3f28 |
author_id_fullname_str_mv |
c83cb65598539b67ba65727ebb3a3f28_***_Felix Richards |
author |
Felix Richards |
author2 |
Elisabeth Sola Pierre-Alain Duc Felix Richards Adeline Paiement Mathias Urbano Julie Klehammer Michal Bílek Jean-Charles Cuillandre Stephen Gwyn Alan McConnachie |
format |
Journal article |
container_title |
Astronomy and Astrophysics |
container_volume |
662 |
container_start_page |
A124 |
publishDate |
2022 |
institution |
Swansea University |
issn |
0004-6361 1432-0746 |
doi_str_mv |
10.1051/0004-6361/202142675 |
publisher |
EDP Sciences |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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description |
Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varying sizes, either through visual inspection or more recently with deep learning. Detailed analyses including photometry have been carried out for a small number of objects, essentially because of the lack of convenient tools able to precisely characterize tidal structures around large samples of galaxies.Aims. Our goal is to characterize in detail, and in particular obtain quantitative measurements, of LSB structures identified in deep images of samples consisting of hundreds of galaxies.Methods. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures with precision, as well as artifacts or foreground structures. All parameters are automatically stored in a database which may be queried to retrieve quantitative measurements. We annotated LSB structures around 352 nearby massive galaxies with deep images obtained with the Canada-France-Hawaii Telescope as part of two large programs: Mass Assembly of early-Type GaLAxies with their fine Structures and Ultraviolet Near Infrared Optical Northern Survey/Canada-France Imaging Survey. Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated with a disrupted satellite, tails that formed in major mergers, ghost reflections, or cirrus.Results. From our database containing 8441 annotations, the area, size, median surface brightness, and distance to the host of 228 structures were computed. The results confirm the fact that tidal structures defined as streams are thinner than tails, as expected by numerical simulations. In addition, tidal tails appear to exhibit a higher surface brightness than streams (by about 1 mag), which may be related to different survival times for the two types of collisional debris. We did not detect any tidal feature fainter than 27.5 magarcsec−2, while the nominal surface brightness limits of our surveys range between 28.3 and 29 magarcsec−2, a difference that needs to be taken into account when estimating the sensitivity of future surveys to identify LSB structures.Conclusions. We compiled an annotation database of observed LSB structures around nearby massive galaxies including tidal features that may be used for quantitative analysis and as a training set for machine learning algorithms. |
published_date |
2022-07-01T04:18:44Z |
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11.035655 |