用扩散模型与循环推理机联合采样引力透镜源与质量分布像素图。
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
- 结合扩散生成模型与循环推理机，直接生成源星系和前景质量分布的联合后验样本。
- 在高分辨率、高信噪比条件下，能精准拟合真实宇宙模拟中的引力透镜数据至噪声水平。
- 适用于高维非线性引力透镜反演，适合天体物理中精确重建星系结构的研究者。
对星系-星系强引力透镜建模以推断源星系亮度和前景星系质量分布，计算上极具挑战性，尤其在高分辨率、高信噪比观测下。此情况下需高维表示源和前景质量分布，才能将数据拟合至噪声水平。该反演问题因维度高且前景质量分布具有非线性，对传统及基于机器学习的方法均构成挑战。本文提出一种方法，可基于观测生成源星系与前景质量分布的联合后验像素图像样本。该方法结合扩散生成建模与循环推理机，能对来自宇宙学流体动力学模拟的真实引力透镜模拟进行建模，实现对噪声水平的精确拟合。
原文摘要 · Abstract (English)
Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise ratio observations. In this regime, high-dimensional representations of both the source and the foreground mass distribution are necessary to model the data down to the noise level. This inference problem has been challenging for both traditional and machine learning-based methods because of its high dimensionality and its non-linearity in the foreground mass distribution. We present a method to generate joint posterior samples of the source galaxy and foreground mass distribution as pixelated images conditioned on observations. The method combines diffusion-based generative modeling and recurrent inference machines. It can model realistic gravitational lensing simulations with background and foreground galaxies drawn from cosmological hydrodynamical simulations down to the noise level.
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