Weighing the stars: Data-driven stellar population modeling for the next-generation sky surveys

由于新一代天空测量(例如欧几里得、鲁宾/LSST等)，预计很快将有大量的天体物理数据，这将为回答关于暗物质和星系形成的关键问题提供前所未有的机会。这些调查将产生超过20万个强引力透镜，比目前的样本大两个数量级。这样的透镜是星系中暗物质的理想示踪器。该项目旨在开发和利用机器学习方法，对透镜星系进行恒星合成建模。恒星群和强大的透镜模型的结合将使我们能够分离单个系统中透镜的恒星和暗物质成分，并通过分级推理框架确定暗物质和星系恒星成分的群级性质，为暗物质模型和星系形成场景提供独特的约束。引力透镜通过物质(暗物质和重子的组合)在远源图像中引起的引力扭曲来追踪物质在透镜星系中的分布。分离这些星系的暗成分和恒星成分是检验暗物质模型预测和理解星系形成和演化中的重子物理过程的关键。这是通过恒星合成建模来完成的，通过这个过程，恒星光谱模型被组合起来，以适应一组宽带图像。虽然由于各种假设和简并，这些模型仍然存在偏见和不确定性，但最近的研究表明，通过将恒星合成的单独测量与分别探测星系内部和外部的强透镜和弱透镜相结合，对于大量的强透镜系统，有可能从统计上推断暗物质性质和恒星成分的无偏测量，从而能够对暗物质和星系形成模型进行稳健的测试。使用传统的最大似然建模方法，对即将到来的调查中的海量数据(甚至是当前可用的数据)执行这一练习是困难的。然而，最近的进展表明，机器学习可以将镜头建模的过程加快1000多万倍，使计算上难以解决的镜头建模问题在几分钟内得到解决。我们的团队目前正在构建方法和管道来实现这一点。我们将通过获得恒星质量和建立一个分级推断框架来扩展这些努力，该框架结合了对星系恒星数量的测量和对无与伦比的大小样本的透镜参数，以便直接探测重子质量分数、内/外星系质量比的时间演化，以及质量吸积的环境相关性。在预期的全天观测中，欧几里德天文台和鲁宾天文台将于2023年和2024年开始运行，使这一项目变得最及时。最近发射的詹姆斯·韦伯太空望远镜还将提供恒星种群随时间演变的独特特征，这是我们项目的必要投入。我们的跨学科团队由强弱引力透镜分析、恒星群建模和机器学习方面的专家组成，为这一令人兴奋的项目的每个方面形成了独特的专家协作。

Remarkable volumes of astrophysical data are expected soon thanks to a new generation of sky surveys (e.g. Euclid, Rubin/LSST, etc.), providing unprecedented opportunities to answer key questions about dark matter and galaxy formation. These surveys will yield more than 200,000 strong gravitational lenses, two orders of magnitude greater than current samples. Such lenses are ideal tracers of dark matter in galaxies. This project aims to develop and exploit machine learning methods that perform stellar synthesis modeling of lensing galaxies. The combination of stellar population and strong lens modeling will allow us to separate the stellar and dark matter components of lenses in individual systems and, through a hierarchical inference framework, to determine population-level properties of dark matter and stellar components of galaxies, providing unique constraints on dark matter models and galaxy formation scenarios.Gravitational lensing traces the distribution of matter (dark matter and baryons combined) in lensing galaxies through the gravitational distortions they cause in images of distant sources. Separating the dark and stellar components of these galaxies is the key to testing predictions of dark matter models and understanding the baryonic physics processes in galaxy formation and evolution. This is done using stellar synthesis modeling, a procedure by which model stellar spectra are combined to fit a set of broadband images. While these models remain biased and uncertain due to various assumptions and degeneracies, recent studies have demonstrated that by combining the individual measurements of stellar synthesis with strong and weak lensing, which probe the inner and outer parts of a galaxy respectively, for a large population of strong lensing systems, it becomes possible to statistically infer unbiased measurements of dark matter properties and stellar components, allowing robust tests of dark matter and galaxy formation models. Performing this exercise for the monumental volumes of data from upcoming surveys (and even for currently available data) is intractable with traditional maximum-likelihood modeling approaches. However, recent advances have shown that machine learning can accelerate the process of lens modeling by more than 10 million times, allowing the computationally intractable lens modeling problem to be solved in minutes. Our team is currently building methods and pipelines to do just that. We will expand upon these efforts by obtaining stellar masses and building a hierarchical inference framework that combines the measurements of galaxy stellar populations and lensing parameters to samples of unparalleled size in order to directly probe the time evolution of the baryonic mass fraction, the inner/outer galaxy mass ratio, and the environmental dependence of mass accretion. Among the anticipated all-sky surveys, the Euclid and Rubin Observatories should begin operations in 2023 and 2024, making this project most timely. The recently launched James Webb Space Telescope will also provide a unique characterization of the evolution of stellar populations with time, a necessary input for our project. Our interdisciplinary team is composed of experts in strong+weak gravitational lensing analysis, stellar population modeling, and machine learning, forming a unique collaboration of experts for each aspect of this exciting project.