We present a technique to detect optical transients based on an artificial neural networks method. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and then tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The toy-model pipeline that we present here is not yet able to replace human inspection, but it might provide useful hints to identify potential candidates. The method will be further expanded and tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.

中文翻译：

---

使用人工神经网络和来自不同调查的参考图像检测光学瞬变

我们提出了一种基于人工神经网络方法检测光学瞬变的技术。我们描述了两个网络的架构，能够比较不同望远镜拍摄的同一部分天空的图像。一幅图像对应于可能存在潜在瞬态的时期；另一个是早期时代的参考图像。我们使用克里斯蒂娜·V·托雷斯博士纪念天文台获得的数据和斯隆数字巡天的档案参考图像。我们在模拟源样本上训练了一个卷积神经网络和一个密集层网络，然后在从真实图像数据创建的样本上测试了训练后的网络。自主检测方法取代了检测瞬变的标准过程，这通常是通过对差异图像进行源提取，然后对检测到的候选者进行人工检查来实现的。用完全自主的方法代替人工检查组件将允许对感兴趣的机会目标进行快速和自动的跟踪。我们在这里展示的玩具模型管道还不能取代人工检查，但它可能会提供有用的提示来识别潜在的候选者。该方法将在参与南方合作瞬态光学机器人天文台的望远镜上进一步扩展和测试。我们在这里展示的玩具模型管道还不能取代人工检查，但它可能会提供有用的提示来识别潜在的候选者。该方法将在参与南方合作瞬态光学机器人天文台的望远镜上进一步扩展和测试。我们在这里展示的玩具模型管道还不能取代人工检查，但它可能会提供有用的提示来识别潜在的候选者。该方法将在参与南方合作瞬态光学机器人天文台的望远镜上进一步扩展和测试。