We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification without requiring a template subtracted (or difference) image which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the "real-bogus" classification, (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses "image triplets" (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input finding that the testing accuracy is reduced from 96% to 91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for "real-bogus" classification that rely exclusively on the imaging data and require no feature engineering task; (2) demonstrates that high-accuracy (> 90%) models can be built without the need to construct difference images, but some accuracy is lost. Since once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the Difference Image Analysis entirely.

翻译：我们研究了卷积神经网络（CNN）在无需模板减法图像（即差异图像，其生成需借助计算密集型流程——在大数据量的小空间尺度上进行图像匹配）的情况下，实现天体物理瞬变信号与图像伪影分离（即“真实-虚假”分类）的潜力。利用暗能量巡天的数据，我们探索了CNN在以下两方面的应用：(1) 自动化“真实-虚假”分类；(2) 降低瞬变天体发现的算力成本。我们比较了两种架构相似的CNN效率：一种采用“图像三元组”（模板、搜索图像与差异图像），另一种仅以模板和搜索图像作为输入。我们测算了因输入信息缺失导致的效率下降程度，发现测试准确率从96%降至91.1%。进一步通过显著性图分析，探究了后者模型如何从模板与搜索图像中学习所需信息。我们的工作：(1) 证实CNN是仅依赖成像数据、无需特征工程即可完成“真实-虚假”分类的优异模型；(2) 表明无需构建差异图像也能构建高精度（>90%）模型，但会损失部分精度。由于训练后的神经网络能以极低计算成本生成预测结果，我们认为该方法未来在同步巡天项目（如鲁宾天文台时空遗产巡天）中，可完全绕过差异图像分析流程，显著减少瞬变天体探测的算力成本。