Journal articles
Identifying transient and variable sources in radio images
A. Rowlinson
1
A. Stewart
2
J.W. Broderick
3
J.D. Swinbank
4
A. Wijers
1
D. Carbone
5
Y. Cendes
6
R. Fender
7
A. van der Horst
8
G. Molenaar
9
B. Scheers
10
T. Staley
S. Farrell
Jean-Mathias Grießmeier
11
M. Bell
12
J. Eislöffel
13
J. Law
J. van Leeuwen
14
P. Zarka
15, 16
S. Farrell
Author
Jean-Mathias Grießmeier 11
Author Employer institution : Université d'Orléans IdHAL : jean-mathias-griessmeier VIAF : https://viaf.org/viaf/20837572 arXiv : https://arxiv.org/a/griessmeier IdRef : https://www.idref.fr/235780871 ISNI : http://isni.org/isni/0000000020640428 ORCID : https://orcid.org/0000-0003-3362-7996
M. Bell 12
Author
J. Eislöffel 13
Author
1
Anton Pannekoek Institute for Astronomy (Science Park 904, NL-1098 XH Amsterdam - Netherlands)
- UvA - University of Amsterdam [Amsterdam] (Spui 21 1012 WX Amsterdam - Netherlands)
2
LASP - Laboratory for Atmospheric and Space Physics [Boulder] (1234 Innovation Drive, Boulder, CO 80303-7814 - United States)
- University of Colorado [Boulder] (Boulder, Colorado 80309 - United States)
3
SIfA - Sydney Institute for Astronomy (School of Physics, University of Sydney, NSW 2006, Australia - Australia)
- The University of Sydney (New South Wales 2006 - Australia)
4
University of Washington [Seattle] (Seattle, Washington 98105 - United States)
5
Department of Physics and Astronomy (United States)
- TTU - Texas Tech University [Lubbock] (2500 Broadway, Lubbock, TX 79409 - United States)
6
Dunlap Institute for Astronomy and Astrophysics [Toronto] (University of Toronto, 50 St George Street, Toronto, Ontario M5S 3H4, Canada - Canada)
- University of Toronto (27 King's College Circle, Toronto M5S 1A1 - Canada)
7
Oxford Astrophysics (Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK - United Kingdom)
- University of Oxford [Oxford] (Wellington Square, Oxford OX1 2JD - United Kingdom)
8
Department of Physics the George Washington University (United States)
9
Department of Physics and Electronics (South Africa)
- Rhodes University, Grahamstown (Rhodes University • Rhodes University, P.O. Box 94 Grahamstown 6140 South Africa - South Africa)
10
CWI - Centrum Wiskunde & Informatica (Science Park 123, 1098 XG Amsterdam - Netherlands)
11
LPC2E - Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (3A, Avenue de la Recherche Scientifique 45071 Orléans cedex 2 - France)
- OSUC - Observatoire des Sciences de l'Univers en région Centre : UMS3116 (Campus Géosciences, 1A rue de la Férollerie, 45071 Orléans - France)
- INSU - CNRS - Institut national des sciences de l'Univers (INSU-CNRS 3 rue Michel-Ange, 75794 Paris Cedex 16 - France)
- Observatoire de Paris (61 Av de l'Observatoire 75014 PARIS - France)
- PSL - Université Paris sciences et lettres (60 rue Mazarine 75006 Paris - France)
- UO - Université d'Orléans (Château de la Source - Avenue du Parc Floral - BP 6749 - 45067 Orléans cedex 2 - France)
- CNRS - Centre National de la Recherche Scientifique : UMS3116 (France)
- CNES - Centre National d’Études Spatiales [Paris] : UMR 7328 (Siège social : 2 place Maurice Quentin, 75039 Paris - France)
12
UTS - University of Technology Sydney (City campus
15 Broadway
Ultimo NSW 2007 - Australia)
13
TLS - Thüringer Landessternwarte Tautenburg ( Sternwarte 5, 07778 Tautenburg - Germany)
14
Department of Astronomy [Berkeley] (601 Campbell Hall, Berkeley, CA 94720-3411 - United States)
- University of California [Berkeley] (Berkeley, CA - United States)
- University of California (United States)
15
LESIA (UMR_8109) - Laboratoire d'études spatiales et d'instrumentation en astrophysique (5, place Jules Janssen 92190 MEUDON - France)
- INSU - CNRS - Institut national des sciences de l'Univers (INSU-CNRS 3 rue Michel-Ange, 75794 Paris Cedex 16 - France)
- Observatoire de Paris (61 Av de l'Observatoire 75014 PARIS - France)
- PSL - Université Paris sciences et lettres (60 rue Mazarine 75006 Paris - France)
- UPD7 - Université Paris Diderot - Paris 7 : UMR_8109 (5 rue Thomas-Mann - 75205 Paris cedex 13 - France)
- SU - Sorbonne Université (21 rue de l’École de médecine - 75006 Paris - France)
- CNRS - Centre National de la Recherche Scientifique : UMR8109 (France)
16
USN - Unité Scientifique de la Station de Nançay (18330 NANCAY - France)
- OSUC - Observatoire des Sciences de l'Univers en région Centre (Campus Géosciences, 1A rue de la Férollerie, 45071 Orléans - France)
- INSU - CNRS - Institut national des sciences de l'Univers (INSU-CNRS 3 rue Michel-Ange, 75794 Paris Cedex 16 - France)
- Observatoire de Paris (61 Av de l'Observatoire 75014 PARIS - France)
- PSL - Université Paris sciences et lettres (60 rue Mazarine 75006 Paris - France)
- UO - Université d'Orléans (Château de la Source - Avenue du Parc Floral - BP 6749 - 45067 Orléans cedex 2 - France)
- CNRS - Centre National de la Recherche Scientifique : UMS3116 (France)
- INSU - CNRS - Institut national des sciences de l'Univers : USR 704 (INSU-CNRS 3 rue Michel-Ange, 75794 Paris Cedex 16 - France)
- Observatoire de Paris : USR 704 (61 Av de l'Observatoire 75014 PARIS - France)
- PSL - Université Paris sciences et lettres (60 rue Mazarine 75006 Paris - France)
- UO - Université d'Orléans : USR 704 (Château de la Source - Avenue du Parc Floral - BP 6749 - 45067 Orléans cedex 2 - France)
- CNRS - Centre National de la Recherche Scientifique : USR 704 (France)
Abstract : With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. We use a simple anomaly detection algorithm and a penalised logistic regression algorithm. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as Python tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools).
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https://hal-insu.archives-ouvertes.fr/insu-02123783
Contributor : Nathalie Pothier <>
Submitted on : Tuesday, March 31, 2020 - 10:52:49 AM
Last modification on : Thursday, September 24, 2020 - 10:04:02 AM
Contributor : Nathalie Pothier <>
Submitted on : Tuesday, March 31, 2020 - 10:52:49 AM
Last modification on : Thursday, September 24, 2020 - 10:04:02 AM
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Identifiers
- HAL Id : insu-02123783, version 1
- DOI : 10.1016/j.ascom.2019.03.003
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UNIV-PARIS | INSU | SU-SCIENCES | USPC | UNIV-PARIS7 | SORBONNE-UNIVERSITE | OBSPM | CNRS | USN | LPC2E | UNIV-ORLEANS | OSUC | LESIA | UP-SCIENCES | PSL
Citation
A. Rowlinson, A. Stewart, J.W. Broderick, J.D. Swinbank, A. Wijers, et al.. Identifying transient and variable sources in radio images. Astronomy and Computing, Elsevier 2019, 27, pp.111-129. ⟨10.1016/j.ascom.2019.03.003⟩. ⟨insu-02123783⟩
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