Identifying transient and variable sources in radio images

Rowlinson, A; Stewart, AJ; Broderick, JW; Swinbank, JD; Wijers, RAMJ; Carbone, D; Cendes, Y; Fender, R; van der Horst, A; Molenaar, G; Scheers, B; Staley, T; Farrell, S; Grießmeier, JM; Bell, M; Eislöffel, J; Law, CJ; van Leeuwen, J; Zarka, P

Identifying transient and variable sources in radio images

Rowlinson, A Stewart, AJ Broderick, JW Swinbank, JD Wijers, RAMJ Carbone, D Cendes, Y Fender, R van der Horst, A Molenaar, G Scheers, B Staley, T Farrell, S Grießmeier, JM Bell, M

Eislöffel, J Law, CJ van Leeuwen, J Zarka, P

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Publication Type:: Journal Article
Citation:: Astronomy and Computing, 2019, 27 pp. 111 - 129
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Rowlinson, A	en_US
dc.contributor.author	Stewart, AJ	en_US
dc.contributor.author	Broderick, JW	en_US
dc.contributor.author	Swinbank, JD	en_US
dc.contributor.author	Wijers, RAMJ	en_US
dc.contributor.author	Carbone, D	en_US
dc.contributor.author	Cendes, Y	en_US
dc.contributor.author	Fender, R	en_US
dc.contributor.author	van der Horst, A	en_US
dc.contributor.author	Molenaar, G	en_US
dc.contributor.author	Scheers, B	en_US
dc.contributor.author	Staley, T	en_US
dc.contributor.author	Farrell, S	en_US
dc.contributor.author	Grießmeier, JM	en_US
dc.contributor.author	Bell, M https://orcid.org/0000-0003-1767-5277	en_US
dc.contributor.author	Eislöffel, J	en_US
dc.contributor.author	Law, CJ	en_US
dc.contributor.author	van Leeuwen, J	en_US
dc.contributor.author	Zarka, P	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Astronomy and Computing, 2019, 27 pp. 111 - 129	en_US
dc.identifier.issn	2213-1337	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135442
dc.description.abstract	© 2019 Elsevier B.V. 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).	en_US
dc.relation.ispartof	Astronomy and Computing	en_US
dc.relation.isbasedon	10.1016/j.ascom.2019.03.003	en_US
dc.title	Identifying transient and variable sources in radio images	en_US
dc.type	Journal Article
utslib.citation.volume	27	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0201 Astronomical and Space Sciences	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 2019 Elsevier B.V. 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).

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/135442