The Library
Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks — sifting the GOTO candidate stream
Tools
Tools
Tools
(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.
|
PDF
WRAP-Transient-optimised-real-bogus-Bayesian-Convolutional-Neural-Networks-2021.pdf - Accepted Version - Requires a PDF viewer. Download (1890Kb) | Preview |
Official URL: https://doi.org/10.1093/mnras/stab633
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: |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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: |
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Is Part Of: | 1 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related URLs: | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
