利用机器学习加速引力透镜质量模型：在X射线天文学中的应用
Speeding up Gravitational Lens Mass Models with Machine Learning: Applications in X-ray Astronomy
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中文总结 AI 辅助
研究利用机器学习加速四重透镜类星体质量模型参数推断，通过模拟训练神经网络预测质量参数和椭圆率，应用于多个四重透镜类星体，提高定位精度和角分辨率，加速质量建模并能应用于新系统。
中文摘要 AI 辅助
对四重透镜类星体的多波段观测是宇宙学、视线方向暗物质子结构以及高红移类星体X射线发射区域结构的有力探测手段。这些研究依赖于获取准确的物质表面质量密度模型。我们提出一种基于模拟的机器学习方法，通过模拟具有奇异等温椭球（SIE）透镜的四重透镜源网格，利用四个透镜图像的投影位置训练两个全连接神经网络来预测质量参数和椭圆率，可将真实四重透镜系统中的参数推断加速几个数量级。对于大部分模拟系统，神经网络初始化的质量模型在几分钟内收敛，能在<0.''005水平恢复源位置。我们将神经网络应用于七个有存档钱德拉观测数据的四重透镜类星体，最终优化的质量模型能预测盖亚数据发布3中观测到的透镜图像位置，使焦散方法能在这些原本无法分辨的系统中将X射线到光学发射区域定位到毫角秒精度，将钱德拉在高红移时的有效角分辨率提高多达两个数量级。我们的方法通过提供有信息的初始参数加速了质量建模，可应用于即将到来的调查中预期的许多新四重透镜系统。
英文摘要
Multi-wavelength observations of quadruply lensed quasars constitute a powerful probe of cosmology, dark matter substructure along the line of sight, and the structure of X-ray emitting regions in high-redshift quasars. These investigations are conditional on acquiring an accurate model for the surface mass density of matter lensing these quasars. We propose a simulation-based machine learning method to accelerate parameter inference in real quadruply lensed systems by several orders of magnitude. We simulate a grid of quadruply lensed sources with Singular Isothermal Ellipsoid (SIE) lenses and use the projected positions of the four lensed images to train two fully connected neural networks that predict the mass parameter and ellipticity. For a large fraction of simulated systems, the neural network-initialised mass models converge in time-scales of a few minutes and recover the source position at the <0.''005 level for a broad range of lens masses and ellipticities. We apply our neural networks to seven quadruply lensed quasars, lensed by isolated galaxies or a galaxy-perturber pair, which have archival Chandra observations. The final optimised mass models for each quasar predict the observed lensed image positions in Gaia Data Release 3. These mass models enable the caustic method, which locates the X-ray-to-optical emission regions to milliarcsecond precision in these otherwise unresolvable systems, improving the effective angular resolution of Chandra at high-z by up to two orders of magnitude. Our approach accelerates this mass modelling by supplying informed initial parameters, enabling application to the many new quadruply lensed systems expected from forthcoming surveys.
