论文标题
旨在解决宇宙剪切向前建模中的模型偏差
Towards solving model bias in cosmic shear forward modeling
论文作者
论文摘要
随着现代星系调查的体积和质量的增加，测量印记在星系形状的宇宙学信号的困难也是如此。宇宙中最庞大的结构产生的弱重力镜头产生了一种称为宇宙剪切的星系形态的轻微剪切，这是宇宙学模型的关键探针。基于椭圆度测量统计的剪切估计技术的现代技术遭受了以下事实：椭圆度不是任意星系光谱的明确定义的数量，从而偏向剪切估计。我们表明，混合物理和深度学习的分层贝叶斯模型，生成模型捕获了银河系的形态，使我们能够恢复对逼真的星系上剪切的无偏估计，从而解决了模型偏置。
As the volume and quality of modern galaxy surveys increase, so does the difficulty of measuring the cosmological signal imprinted in galaxy shapes. Weak gravitational lensing sourced by the most massive structures in the Universe generates a slight shearing of galaxy morphologies called cosmic shear, key probe for cosmological models. Modern techniques of shear estimation based on statistics of ellipticity measurements suffer from the fact that the ellipticity is not a well-defined quantity for arbitrary galaxy light profiles, biasing the shear estimation. We show that a hybrid physical and deep learning Hierarchical Bayesian Model, where a generative model captures the galaxy morphology, enables us to recover an unbiased estimate of the shear on realistic galaxies, thus solving the model bias.
