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

@article{Killestein2021TransientoptimizedRC,
title={Transient-optimized real-bogus classification with Bayesian convolutional neural networks – sifting the GOTO candidate stream},
author={Thomas L. Killestein and Joseph D. Lyman and D. Steeghs and Kendall Ackley and Martin J Dyer and Krzysztof Ulaczyk and Ry Cutter and Y.-L. Mong and Duncan K. Galloway and Vik S. Dhillon and P. T. O’Brien and Gavin Ramsay and Saran Poshyachinda and Rubina Kotak and Rene P. Breton and Laura K Nuttall and Enric Pall'e and Don Pollacco and Eric Thrane and Suparerk Aukkaravittayapun and Supachai Awiphan and U. Burhanudin and Paul Robin Brian Chote and A. A. Chrimes and E J Daw and C Duffy and R A J Eyles-Ferris and Benjamin P. Gompertz and Teppo Heikkila and P. Irawati and Mark R. Kennedy and Andrew J. Levan and Stuart P. Littlefair and L. Makrygianni and Daniel Mata S'anchez and Seppo Mattila and Justyn R. Maund and J. McCormac and David Mkrtichian and James R. Mullaney and Evert Rol and Utane Sawangwit and Elizabeth R. Stanway and Rhaana L. C. Starling and Paul A. Str{\o}m and S M Tooke and Klaas Wiersema and S. C. Williams},
journal={Monthly Notices of the Royal Astronomical Society},
year={2021}
}
• Published 19 February 2021
• Computer Science
• Monthly Notices of the Royal Astronomical Society
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…
9 Citations

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