Machine Learning for Transient Recognition in Difference Imaging With Minimum Sampling Effort
Abstract
The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe 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 presentan approach for creating a training set by using all detections in the science images to be thesample 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-by-21pixel stamps centered 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% prediction accuracy on the real detections at a false alarm rate of 1%.
Keywords
Cite
@article{arxiv.2008.10178,
title = {Machine Learning for Transient Recognition in Difference Imaging With Minimum Sampling Effort},
author = {Yik-Lun Mong and Kendall Ackley and Duncan Galloway and Tom Killestein and Joe Lyman and Danny Steeghs and Vik Dhillon and Paul O'Brien and Gavin Ramsay and Saran Poshyachinda and Rubina Kotak and Laura Nuttall and Enric Pall'e and Don Pollacco and Eric Thrane and Martin Dyer and Krzysztof Ulaczyk and Ryan Cutter and James McCormac and Paul Chote and Andrew Levan and Tom Marsh and Elizabeth Stanway and Ben Gompertz and Klaas Wiersema and Ashley Chrimes and Alexander Obradovic and James Mullaney and Ed Daw and Stuart Littlefair and Justyn Maund and Lydia Makrygianni and Umar Burhanudin and Rhaana Starling and Rob Eyles and Spencer Tooke and Christopher Duffy and Suparerk Aukkaravittayapun and Utane Sawangwit and Supachai Awiphan and David Mkrtichian and Puji Irawati and Seppo Mattila and Teppo Heikkil"a and Rene Breton and Mark Kennedy and Daniel Mata-Sanchez and Evert Rol},
journal= {arXiv preprint arXiv:2008.10178},
year = {2020}
}
Comments
9 pages, 8 figures