DeepGhostBusters：使用掩模R-CNN检测和掩模光学勘测图像中的重影和散射光伪影
DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images
摘要：
宽视场天文测量经常受到不需要的反射（通常称为“重影伪影”或“鬼”）和散射光伪影的影响。这些文物的识别和减轻是重要的微弱和低表面亮度系统的严格的天文分析。然而，由于a）这些特征的复杂形态和b）当前和不久的将来调查的大数据量，鬼和散射光伪影的识别是具有挑战性的。在这项工作中，我们使用暗能量调查（DES）的图像来训练，验证和测试深度神经网络（Mask R-CNN），以检测和定位鬼魂和散射光伪影。我们发现，Mask R-CNN模型识别受影响区域的能力优于传统算法和传统卷积神经网络方法。我们建议将Mask R-CNN分割与经典的CNN分类器相结合的多步流水线为当前和不久的将来的调查中的重影和散射光伪影的自动检测提供了强大的技术。
英文摘要：
Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.
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