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The advent of wide-field sky surveys has led to the growth of transient andvariable source discoveries. The data deluge produced by these surveys hasnecessitated the use of machine learning (ML) and deep learning (DL) algorithmsto sift through the vast incoming data stream. A problem that arises inreal-world applications of learning algorithms for classification is imbalanceddata, where a class of objects within the data is underrepresented, leading toa bias for over-represented classes in the ML and DL classifiers. We present arecurrent neural network (RNN) classifier that takes in photometric time-seriesdata and additional contextual information (such as distance to nearby galaxiesand on-sky position) to produce real-time classification of objects observed bythe Gravitational-wave Optical Transient Observer (GOTO), and use analgorithm-level approach for handling imbalance with a focal loss function. Theclassifier is able to achieve an Area Under the Curve (AUC) score of 0.972 whenusing all available photometric observations to classify variable stars,supernovae, and active galactic nuclei. The RNN architecture allows us toclassify incomplete light curves, and measure how performance improves as moreobservations are included. We also investigate the role that contextualinformation plays in producing reliable object classification.

Light curve classification with recurrent neural networks for GOTO: dealing with imbalanced data