Astronomical images are often plagued by unwanted artifacts that arise from a number of sources including imperfect optics, faulty image sensors, cosmic ray hits, and even airplanes and artificial satellites. Spurious reflections (known as “ghosts”) and the scattering of light off the surfaces of a camera and/or telescope are particularly difficult to avoid. Detecting ghosts and scattered light efficiently in large cosmological surveys that will acquire petabytes of data can be a daunting task. In this paper, we use data from the Dark Energy Survey to develop, train, and validate a machine learning model to detect ghosts and scattered light using convolutional neural networks. The model architecture and training procedure are discussed in detail, and the performance on the training and validation set is presented. Testing is performed on data and results are compared with those from a ray-tracing algorithm. As a proof of principle, we have shown that our method is promising for the Rubin Observatory and beyond.

天文图像经常受到不想要的伪影的困扰，这些伪影来自许多来源，包括不完美的光学元件、有缺陷的图像传感器、宇宙射线撞击，甚至飞机和人造卫星。杂散反射（称为“鬼影”）和相机和/或望远镜表面的光散射特别难以避免。在将获得 PB 级数据的大型宇宙学调查中有效地检测鬼影和散射光可能是一项艰巨的任务。在本文中，我们使用来自暗能量调查的数据来开发、训练和验证机器学习模型，以使用卷积神经网络检测鬼影和散射光。详细讨论了模型架构和训练过程，并介绍了训练和验证集的性能。对数据进行测试，并将结果与​​光线追踪算法的结果进行比较。作为原理证明，我们已经证明我们的方法对鲁宾天文台及其他地方很有希望。