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Machine learning for transient recognition in difference imaging with minimum sampling effort

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journal contribution
posted on 04.02.2021, 15:21 by Y-L Mong, K Ackley, DK Galloway, T Killestein, J Lyman, D Steeghs, V Dhillon, PT O'Brien, G Ramsay, S Poshyachinda, R Kotak, L Nuttall, E Palle, D Pollacco, E Thrane, MJ Dyer, K Ulaczyk, R Cutter, J McCormac, P Chote, AJ Levan, T Marsh, E Stanway, B Gompertz, K Wiersema, A Chrimes, A Obradovic, J Mullaney, E Daw, S Littlefair, J Maund, L Makrygianni, U Burhanudin, RLC Starling, RAJ Eyles-Ferris, S Tooke, C Duffy, S Aukkaravittayapun, U Sawangwit, S Awiphan, D Mkrtichian, P Irawati, S Mattila, T Heikkila, R Breton, M Kennedy, D Mata Sanchez, E Rol
The amount of observational data produced by time-domain astronomy is exponentially increasing. Human inspection alone is not an effective way to identify genuine transients from the data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We present an approach for creating a training set by using all detections in the science images to be the sample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21 × 21 pixel stamps centred at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95 per cent prediction accuracy on the real detections at a false alarm rate of 1 per cent⁠.

History

Citation

Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 4, December 2020, Pages 6009–6017, https://doi.org/10.1093/mnras/staa3096

Author affiliation

Department of Physics and Astronomy

Version

VoR (Version of Record)

Published in

Monthly Notices of the Royal Astronomical Society

Volume

499

Issue

4

Pagination

6009 - 6017

Publisher

Oxford University Press (OUP)

issn

0035-8711

eissn

1365-2966

Acceptance date

05/10/2020

Copyright date

2020

Available date

09/10/2020

Language

English