MeerCRAB: MeerLICHT Classification of Real and Bogus Transients using Deep Learning

Astronomers require efficient automated detection
and classification pipelines when conducting large-scale surveys of the (optical) sky for variable and transient sources. Such pipelines are fundamentally important, as they permit
rapid follow-up and analysis of those detections most likely to
be of scientific value. We therefore present a deep learning
pipeline based on the convolutional neural network architecture called MeerCRAB. It is designed to filter out the so called “bogus” detections from true astrophysical sources in the transient detection pipeline of the MeerLICHT telescope.
Optical candidates are described using a variety of 2D images
and numerical features extracted from those images. The relationship between the input images and the target classes is
unclear, since the ground truth is poorly defined and often the
subject of debate. This makes it difficult to determine which
source of information should be used to train a classification
algorithm. We therefore used two methods for labelling our
data (i) thresholding and (ii) latent class model approaches.
We deployed variants of MeerCRAB that employed different
network architectures trained using different combinations
of input images and training set choices, based on classification labels provided by volunteers. The deepest network worked best with an accuracy of 99.5% and Matthews correlation coefficient (MCC) value of 0.989. The best model was integrated to the MeerLICHT transient vetting pipeline, enabling the accurate and efficient classification of detected transients that allows researchers to select the most promising
candidates for their research goals.