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Transient-optimized real-bogus classification with Bayesian convolutional neural networks - sifting the GOTO candidate stream

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posted on 01.07.2021, 13:23 by TL Killestein, J Lyman, D Steeghs, K Ackley, MJ Dyer, K Ulaczyk, R Cutter, Y-L Mong, DK Galloway, V Dhillon, P O'Brien, G Ramsay, S Poshyachinda, R Kotak, RP Breton, LK Nuttall, E Palle, D Pollacco, E Thrane, S Aukkaravittayapun, S Awiphan, U Burhanudin, P Chote, A Chrimes, E Daw, C Duffy, R Eyles-Ferris, B Gompertz, T Heikkila, P Irawati, MR Kennedy, A Levan, S Littlefair, L Makrygianni, D Mata Sanchez, S Mattila, J Maund, J McCormac, D Mkrtichian, J Mullaney, E Rol, U Sawangwit, E Stanway, R Starling, PA Strom, S Tooke, K Wiersema, SC Williams
Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritize human vetting efforts and inform future model optimization via active learning. To fully realize the potential of this architecture, we present a fully automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1 per cent) compared against classifiers trained with fully human-labelled data sets, while being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community.

History

Citation

Monthly Notices of the Royal Astronomical Society, Volume 503, Issue 4, June 2021, Pages 4838–4854, https://doi.org/10.1093/mnras/stab633

Author affiliation

Department of Physics and Astronomy

Version

VoR (Version of Record)

Published in

Monthly Notices of the Royal Astronomical Society

Volume

503

Issue

4

Pagination

4838 - 4854

Publisher

Oxford University Press (OUP) for Royal Astronomical Society

issn

0035-8711

eissn

1365-2966

Acceptance date

01/03/2021

Copyright date

2021

Available date

01/07/2021

Language

English