标题： 星系形状测量的卷积神经网络 显示英文标题

标题： Galaxy shape measurement with convolutional neural networks

摘要： 我们展示了从暗能量调查（DES）第一数据发布（DES DR1）的广域巡天图像中训练和评估卷积神经网络（CNN）以预测星系形状的结果。 我们使用重叠、更深的巡天中的传统形状测量作为真实值，该巡天覆盖范围更小，即加拿大-法国-夏威夷望远镜引力透镜巡天（CFHTLenS）。 我们证明了从单波段DES图像得到的CNN预测在明亮亮度下能够再现CFHTLenS的结果，并且在较暗亮度下与CFHTLenS的相关性高于DES Y1 im3shape目录中的最大似然模型拟合估计。 使用CNN预测形状参数也非常快速，每个星系仅需0.2毫秒，比正向模型拟合提高了4个数量级以上。 当使用同一星系的多张图像，甚至不同颜色波段的图像时，CNN也能准确预测形状，且没有额外的计算开销。 CNN对于暗弱天体的精度再次更高，并且与DES Y1元校准目录相比，CNN的优势在蓝色星系中比红色星系更为明显，后者使用riz波段图像拟合单一高斯轮廓。 我们证明，在元校准自校准框架内的CNN形状预测能够产生具有可忽略的乘法偏差，$ m < 10^{-3}$，且没有显著的点扩散函数泄漏。 我们提出的设置适用于当前和下一代弱引力透镜巡天，在专用深场中可以测量更高品质的真实形状。 显示英文摘要

摘要： We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape measurements as ground truth from an overlapping, deeper survey with less sky coverage, the Canada-France Hawaii Telescope Lensing Survey (CFHTLenS). We demonstrate that CNN predictions from single band DES images reproduce the results of CFHTLenS at bright magnitudes and show higher correlation with CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is also extremely fast, it takes only 0.2 milliseconds per galaxy, improving more than 4 orders of magnitudes over forward model fitting. The CNN can also accurately predict shapes when using multiple images of the same galaxy, even in different color bands, with no additional computational overhead. The CNN is again more precise for faint objects, and the advantage of the CNN is more pronounced for blue galaxies than red ones when compared to the DES Y1 metacalibration catalogue, which fits a single Gaussian profile using riz band images. We demonstrate that CNN shape predictions within the metacalibration self-calibrating framework yield shear estimates with negligible multiplicative bias, $ m < 10^{-3}$, and no significant PSF leakage. Our proposed setup is applicable to current and next generation weak lensing surveys where higher quality ground truth shapes can be measured in dedicated deep fields.