使用扩散模型和循环推理机在像素空间中进行强引力透镜后验采样
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
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中文总结 AI 辅助
研究星系-星系强引力透镜问题，提出结合扩散生成建模与循环推理机的方法，能生成源星系和前景质量分布的联合后验样本作为像素化图像，可对含背景和前景星系的真实引力透镜模拟建模至噪声水平。
中文摘要 AI 辅助
对星系-星系强引力透镜进行建模以推断源星系的亮度和前景星系的质量分布在计算上具有挑战性，特别是对于高分辨率、高信噪比的观测。在这种情况下，需要源和前景质量分布的高维表示才能将数据建模到噪声水平。由于其高维度和前景质量分布的非线性，这个推理问题对传统方法和基于机器学习的方法都具有挑战性。我们提出了一种方法，以观测为条件生成源星系和前景质量分布的联合后验样本作为像素化图像。该方法结合了基于扩散的生成建模和循环推理机。它可以对从宇宙流体动力学模拟中提取的背景和前景星系的真实引力透镜模拟进行建模，直至噪声水平。
英文摘要
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.
发表机构
- Department of Physics, Universit\'e de Montr\'eal, Montreal QC, Canada
- Mila - Quebec AI Institute, Montreal QC, Canada
- Ciela Institute, Institute for Astrophysics
- Trottier Space Institute, McGill University, Montreal QC, Canada
- Center for Computational Astrophysics, Flatiron Institute, New York, USA
- Perimeter Institute for Theoretical Physics, Waterloo ON, Canada
机构由 AI 辅助整理，请以论文原文为准。
