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Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks — sifting the GOTO candidate stream

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Killestein, T., Lyman, J. D., Steeghs, D., Ackley, K., Dyer, M. J., Ulaczyk, Krzysztof, Cutter, Ryan J., Mong, Y -L., Galloway, D. K., Dhillon, V. et al.
(2021) Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks — sifting the GOTO candidate stream. Monthly Notices of the Royal Astronomical Society, 503 (4). pp. 4838-4854. doi:10.1093/mnras/stab633 ISSN 1745-3933.

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Official URL: https://doi.org/10.1093/mnras/stab633

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Abstract

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.

Item Type: Journal Article
Alternative Title:
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Divisions: Faculty of Science, Engineering and Medicine > Science > Physics
Library of Congress Subject Headings (LCSH): Bayesian field theory, Convolutions (Mathematics), Neural networks (Computer science)
Journal or Publication Title: Monthly Notices of the Royal Astronomical Society
Publisher: Oxford University Press
ISSN: 1745-3933
Official Date: June 2021
Dates:
DateEvent
June 2021Published
15 March 2021Available
1 March 2021Accepted
Volume: 503
Number: 4
Page Range: pp. 4838-4854
DOI: 10.1093/mnras/stab633
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): This is a pre-copyedited, author-produced version of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record T L Killestein, J Lyman, D Steeghs, K Ackley, M J Dyer, K Ulaczyk, R Cutter, Y-L Mong, D K Galloway, V Dhillon, P O’Brien, G Ramsay, S Poshyachinda, R Kotak, R P Breton, L K Nuttall, E Pallé, D Pollacco, E Thrane, S Aukkaravittayapun, S Awiphan, U Burhanudin, P Chote, A Chrimes, E Daw, C Duffy, R Eyles-Ferris, B Gompertz, T Heikkilä, P Irawati, M R Kennedy, A Levan, S Littlefair, L Makrygianni, D Mata Sánchez, S Mattila, J Maund, J McCormac, D Mkrtichian, J Mullaney, E Rol, U Sawangwit, E Stanway, R Starling, P A Strøm, S Tooke, K Wiersema, S C Williams, Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream, Monthly Notices of the Royal Astronomical Society, Volume 503, Issue 4, June 2021, Pages 4838–4854, is available online at: https://doi.org/10.1093/mnras/stab633
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 4 March 2021
Date of first compliant Open Access: 4 March 2021
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Monash-Warwick AllianceMonash Universityhttp://dx.doi.org/10.13039/501100001779
UNSPECIFIEDMonash Universityhttp://dx.doi.org/10.13039/501100001779
UNSPECIFIEDUniversity of Warwickhttp://dx.doi.org/10.13039/501100000741
UNSPECIFIEDUniversity of Sheffieldhttp://dx.doi.org/10.13039/501100000858
UNSPECIFIEDUniversity of Leicesterhttp://dx.doi.org/10.13039/501100000738
UNSPECIFIEDArmagh Observatoryhttp://dx.doi.org/10.13039/501100000527
UNSPECIFIEDNational Astronomical Research Institute of Thailandhttps://search.crossref.org/funding?q=100016291&from_ui=yes
UNSPECIFIEDTurun Yliopistohttp://dx.doi.org/10.13039/501100005609
UNSPECIFIEDUniversity Of Manchesterhttp://dx.doi.org/10.13039/501100000770
UNSPECIFIEDUniversity of Portsmouthhttp://dx.doi.org/10.13039/100009153
UNSPECIFIEDInstituto de Astrofísica de Canariashttp://viaf.org/viaf/133196444
UNSPECIFIEDScience and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
ST/T007184/1[STFC] Science and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
ST/T003103/1[STFC] Science and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
ST/P000495/1[STFC] Science and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
MR/T020784/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
715051[ERC] Horizon 2020 Framework Programmehttp://dx.doi.org/10.13039/100010661
UNSPECIFIED[STFC] Science and Technology Facilities Councilhttp://dx.doi.org/10.13039/501100000271
Is Part Of: 1
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