Journal article icon

Journal article

Identification of low surface brightness tidal features in galaxies using convolutional neural networks

Abstract:

Faint tidal features around galaxies record their merger and interaction histories over cosmic time. Due to their low surface brightnesses and complex morphologies, existing automated methods struggle to detect such features and most work to date has heavily relied on visual inspection. This presents a major obstacle to quantitative study of tidal debris features in large statistical samples, and hence the ability to be able to use these features to advance understanding of the galaxy populat...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1093/mnras/sty3232

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics
Sub department:
Astrophysics
Role:
Author
ORCID:
0000-0002-6408-4181
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Sub department:
Astrophysics
Oxford college:
Mansfield College
Role:
Author
ORCID:
0000-0001-5578-359X
Publisher:
Oxford University Press Publisher's website
Journal:
Monthly Notices of the Royal Astronomical Society Journal website
Volume:
483
Issue:
3
Pages:
2968-2982
Publication date:
2018-11-29
Acceptance date:
2018-11-26
DOI:
EISSN:
1365-2966
ISSN:
0035-8711
Source identifiers:
949238
Language:
English
Keywords:
Pubs id:
pubs:949238
UUID:
uuid:9be65c47-fe68-419d-b424-58f7fe95ed8b
Local pid:
pubs:949238
Deposit date:
2019-02-01

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP