  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 prioritise human vetting efforts and inform future model
optimisation via active learning. To fully realise 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%) compared against classifiers trained with fully human-labelled datasets,
whilst 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.
