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arXiv 1908.00543astro-ph.CO
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从DES SV弱透镜数据进行深度学习暗物质地图重建

Deep learning dark matter map reconstructions from DES SV weak lensing data

Niall Jeffrey, François Lanusse, Ofer Lahav, Jean-Luc Starck

备注：
Accepted MNRAS, 7 pages, 5 figures, added interpretation of DeepMass improvement
发表信息：
Monthly Notices of the Royal Astronomical Society, Volume 492, Issue 4, March 2020, Pages 5023-5029
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摘要：

我们提出了使用深度学习从弱透镜观测数据重建暗物质地图的第一个方法。我们训练一个卷积神经网络（CNN）与基于Unet的架构上超过$3.6\times10^5$模拟数据实现与非高斯形状噪声和宇宙学参数变化在一个广泛的先验分布。我们将新创建的DES SV图解释为在给定观测剪切的情况下收敛的后验平均值$P(κ| γ)$的近似。我们的DeepMass方法比现有的质量映射方法更准确。通过8000个模拟DES SV数据实现的验证集，与具有固定功率谱的维纳滤波相比，DeepMass方法将均方误差（MSE）提高了11%。通过N体模拟MICE模拟数据，我们表明，具有最佳已知功率谱的维纳滤波仍然比我们的广义方法提供更差的MSE，而没有输入宇宙学参数;我们表明，这种改进是由收敛中的非线性结构驱动的。随着未来弱透镜数据中更高的星系密度揭示出更多的非线性尺度，深度学习很可能成为Euclid和LSST质量映射的主要方法。

英文摘要：

We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network (CNN) with a Unet based architecture on over $3.6\times10^5$ simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created DES SV map as an approximation of the posterior mean $P(κ| γ)$ of the convergence given observed shear. Our DeepMass method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering with the optimal known power spectrum still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.

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