In this paper we present a two-step neural network model to separate detections of solar system objects from optical and electronic artifacts in data obtained with the “Asteroid Terrestrial-impact Last Alert System” (ATLAS), a near-Earth asteroid sky survey system. A convolutional neural network is used to classify small “postage-stamp” images of candidate detections of astronomical sources into eight classes, followed by a multi-layered perceptron that provides a probability that a temporal sequence of four candidate detections represents a real astronomical source. The goal of this work is to reduce the time delay between Near-Earth Object (NEO) detections and submission to the Minor Planet Center. Due to the rare and hazardous nature of NEOs, a low false negative rate is a priority for the model. We show that the model reaches 99.6% accuracy on real asteroids in ATLAS data with a 0.4% false negative rate. Deployment of this model on ATLAS has reduced the amount of NEO candidates that astronomers must screen by 90%, thereby bringing ATLAS one step closer to full autonomy.

在本文中，我们提出了一个两步神经网络模型，用于将通过近地小行星天空测量系统“小行星地面撞击最后警报系统”（ATLAS）获得的数据中的光学和电子伪影分离出太阳系物体。 。卷积神经网络用于将天文源候选检测的小型“邮戳”图像分为八类，然后是多层感知器，该多层感知器提供了四个候选检测的时间序列代表真实天文源的可能性。这项工作的目的是减少从近地天体（NEO）探测到提交到小行星中心的时间。由于NEO的稀有性和危险性，因此低假阴性率是该模型的优先事项。我们显示该模型达到了99。ATLAS数据中真实小行星的准确度为6％，假阴性率为0.4％。该模型在ATLAS上的部署使天文学家必须筛选的NEO候选对象的数量减少了90％，从而使ATLAS更加接近完全自治。