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.

天文学家在对（光学）天空的可变和瞬态源进行大规模调查时需要高效的自动检测和分类管道。这种管道从根本上来说很重要，因为它们允许对最有可能具有科学价值的检测进行快速跟踪和分析。因此，我们提出了一个基于卷积神经网络架构的深度学习管道，称为MeerCRAB. 它旨在从 MeerLICHT 望远镜瞬态探测管道中的真实天体物理源中滤除所谓的“虚假”探测。使用各种 2D 图像和从这些图像中提取的数值特征来描述光学候选。输入图像和目标类别之间的关系尚不清楚，因为基本事实定义不明确并且经常成为争论的主题。这使得很难确定应该使用哪个信息源来训练分类算法。因此，我们使用了两种方法来标记我们的数据（i）阈值和（ii）潜在类模型方法。我们部署了MeerCRAB 的变体基于志愿者提供的分类标签，采用不同的网络架构，使用不同的输入图像组合和训练集选择进行训练。最深的网络效果最好，准确度为 99.5%，马修斯相关系数 (MCC) 值为 0.989。最佳模型已集成到 MeerLICHT 瞬态审查管道中，从而能够对检测到的瞬态进行准确有效的分类，从而使研究人员能够为其研究目标选择最有希望的候选者。