Euclid 准备阶段。LXVII. 用于弱引力透镜剪切校准的深度学习真实星系形态
Euclid preparation. LXVII. Deep learning true galaxy morphologies for weak lensing shear bias calibration
AI总结：
本文提出基于小波散射变换的深度学习方法，从 HST 数据生成真实形态的 Euclid 模拟星系，并量化了真实形态相对于双重 Sérsic 轮廓在弱引力透镜剪切校准中引入的乘性偏差和检测偏差，证明其对第四阶段巡天不可忽略。
摘要：
迄今为止，用于弱引力透镜巡天的星系图像模拟通常将所有星系的光度轮廓近似为单一或双重 Sérsic 轮廓，忽略了星系子结构以及偏离这种简化参数化表征的形态的影响。虽然这种近似对于以往的数据集可能已经足够，但即将到来的 Euclid 巡天对宇宙剪切校准的严格要求以及数据的高质量，要求我们考虑真实星系子结构对剪切测量偏差的影响。在此，我们提出一种新颖的基于深度学习的方法，直接从 HST 数据中生成此类模拟星系。我们首先构建并验证了一个基于小波散射变换的卷积神经网络，用于学习独立于 HST 星系图像点扩散函数的无噪声表示，这些表示可以被注入到 Euclid 光学仪器 VIS 的图像模拟中，而不会在 PSF 卷积或剪切过程中引入噪声相关性。然后，我们展示了通过从模型中随机采样和条件采样来生成新的星系图像。接下来，我们通过在类似 Euclid 的模拟中比较一组模型天体与其最佳拟合双重 Sérsic 对应体之间的剪切测量偏差，量化了复杂星系形状带来的宇宙剪切偏差。使用 KSB 形状测量算法，我们发现在真实的星等-Sérsic 指数分布下，具有真实形态的分支与参数化轮廓分支之间的乘性偏差差异约为 $6.9\ imes 10^{-3}$。此外，我们发现在用参数化星系和真实星系模拟的完整图像场景之间存在明显的检测偏差差异，导致与形状测量方法无关的 $4.0\ imes 10^{-3}$ 的偏差差异。这使得该问题对于 Euclid 等第四阶段弱引力透镜巡天具有重要意义。
英文摘要：
To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-Sérsic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-Sérsic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid.
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