使用深度神经网络注释大型天文图像目录时的系统性偏差
Systematic biases when using deep neural networks for annotating large catalogs of astronomical images
摘要：
深度卷积神经网络（DCNN）由于其非参数性质、良好的性能以及通过TensorFlow等库的可访问性，已成为自动图像注释的最常见解决方案。在其他领域中，DCNN也是一种常见的方法来注释数字巡天获得的大型天文图像数据库。DCNN的主要缺点之一是复杂的非直观规则，使DCNN充当“黑盒子”，以用户不清楚的方式提供注释。因此，用户通常无法知道DCNN使用什么信息进行分类。在这里，我们证明了DCNN的训练对训练数据的上下文敏感，例如天空中物体的位置。我们发现，对于椭圆星系和螺旋星系的基本分类，用于训练的星系的天空位置影响算法的行为，并导致一个小的，但一致的和统计上显着的偏差。这种偏差以基本星系形态分布的宇宙尺度各向异性的形式表现出来。因此，虽然DCNN是注释扩展源图像的强大工具，但星系形态学训练集的构建应该考虑对象视觉外观以外的更多方面。在任何情况下，用深度神经网络创建的表现出宇宙学各向异性迹象的目录都应该被解释为具有一致性偏差的可能性。
英文摘要：
Deep convolutional neural networks (DCNNs) have become the most common solution for automatic image annotation due to their non-parametric nature, good performance, and their accessibility through libraries such as TensorFlow. Among other fields, DCNNs are also a common approach to the annotation of large astronomical image databases acquired by digital sky surveys. One of the main downsides of DCNNs is the complex non-intuitive rules that make DCNNs act as a ``black box", providing annotations in a manner that is unclear to the user. Therefore, the user is often not able to know what information is used by the DCNNs for the classification. Here we demonstrate that the training of a DCNN is sensitive to the context of the training data such as the location of the objects in the sky. We show that for basic classification of elliptical and spiral galaxies, the sky location of the galaxies used for training affects the behavior of the algorithm, and leads to a small but consistent and statistically significant bias. That bias exhibits itself in the form of cosmological-scale anisotropy in the distribution of basic galaxy morphology. Therefore, while DCNNs are powerful tools for annotating images of extended sources, the construction of training sets for galaxy morphology should take into consideration more aspects than the visual appearance of the object. In any case, catalogs created with deep neural networks that exhibit signs of cosmological anisotropy should be interpreted with the possibility of consistent bias.
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