探索深度神经网络的可解释性，用于使用灵敏度探针寻找引力透镜
Exploring the interpretability of deep neural networks used for gravitational lens finding with a sensitivity probe
摘要：
人工神经网络在天文学中的应用越来越多，但理解这些模型的局限性可能很困难。我们利用一种统计方法，一个敏感性探针，旨在补充建立的方法来解释神经网络的行为，通过量化模型的性能对各种属性的输入的敏感性。我们将这种方法应用于神经网络训练分类图像的星系星系强透镜的暗能量调查。我们发现，网络对颜色、训练中使用的模拟PSF以及透镜光源的光遮挡高度敏感，但对爱因斯坦半径不敏感，并且性能随光源和透镜大小而平滑下降。由此，我们确定了用于约束网络的训练集的弱点，特别是对PSF的过度敏感，并将镜头取景器的选择功能约束为星系光度星等的函数，当镜头源的g波段星等大于21.5时，镜头的r波段星等小于19时，精度显著降低。
英文摘要：
Artificial neural networks are finding increasing use in astronomy, but understanding the limitations of these models can be difficult. We utilize a statistical method, a sensitivity probe, designed to complement established methods for interpreting neural network behavior by quantifying the sensitivity of a model's performance to various properties of the inputs. We apply this method to neural networks trained to classify images of galaxy-galaxy strong lenses in the Dark Energy Survey. We find that the networks are highly sensitive to color, the simulated PSF used in training, and occlusion of light from a lensed source, but are insensitive to Einstein radius, and performance degrades smoothly with source and lens magnitudes. From this we identify weaknesses in the training sets used to constrain the networks, particularly the over-sensitivity to PSF, and constrain the selection function of the lens-finder as a function of galaxy photometric magnitudes, with accuracy decreasing significantly where the g-band magnitude of the lens source is greater than 21.5 and the r-band magnitude of the lens is less than 19.
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