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

中文翻译：

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用于 GOTO 的递归神经网络的光曲线分类：处理不平衡数据

大范围天空调查的出现导致了瞬态和可变源发现的增长。这些调查产生的海量数据需要使用机器学习 (ML) 和深度学习 (DL) 算法来筛选大量传入数据流。在用于分类的学习算法的实际应用中出现的一个问题是数据不平衡，其中数据中的一类对象代表性不足，导致 ML 和 DL 分类器中过度代表性的类存在偏差。我们提出了一个循环神经网络 (RNN) 分类器，它接收光度时间序列数据和附加上下文信息（例如到附近星系的距离和天空位置），以对引力波光学观测到的物体进行实时分类瞬态观察者，并使用算法级别的方法来处理具有焦点损失函数的不平衡。当使用所有可用的光度观测对变星、超新星和活动星系核进行分类时，分类器能够获得 0.972 的曲线下面积 (AUC) 分数。RNN 架构允许我们对不完整的光变曲线进行分类，并衡量在包含更多观察时性能如何提高。我们还研究了上下文信息在产生可靠的对象分类中所起的作用。并衡量在包含更多观察时性能如何提高。我们还研究了上下文信息在产生可靠的对象分类中所起的作用。并衡量在包含更多观察时性能如何提高。我们还研究了上下文信息在产生可靠的对象分类中所起的作用。