论文标题
使用连续神经场的强镜头源重建
Strong Lensing Source Reconstruction Using Continuous Neural Fields
论文作者
论文摘要
从暗物质的性质到宇宙的扩张速率，通过强力透镜扭曲的遥远星系的观察有可能回答天体物理学中一些主要的开放问题。建模星系 - 果实强透镜观测值提出了许多挑战，因为背景源和前景镜头星系的确切配置尚不清楚。一个及时的呼吁，是由许多即将进行的预期高分辨率镜头图像的调查提示的，要求可以有效地以其完整的复杂性对镜头进行建模方法。在这项工作中，我们引入了一种使用连续神经场来非参数重建源银河系的复杂形态的方法，同时在前景镜头星系构型上同时推断分布。我们通过对靶向高分辨率镜头图像的模拟数据进行实验来证明我们的方法的功效，类似于近乎未来的天体物理调查中预期的图像。
From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answer some of the major open questions in astrophysics. Modeling galaxy-galaxy strong lensing observations presents a number of challenges as the exact configuration of both the background source and foreground lens galaxy is unknown. A timely call, prompted by a number of upcoming surveys anticipating high-resolution lensing images, demands methods that can efficiently model lenses at their full complexity. In this work, we introduce a method that uses continuous neural fields to non-parametrically reconstruct the complex morphology of a source galaxy while simultaneously inferring a distribution over foreground lens galaxy configurations. We demonstrate the efficacy of our method through experiments on simulated data targeting high-resolution lensing images similar to those anticipated in near-future astrophysical surveys.
