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

称量恒星：用于下一代巡天的数据驱动的恒星种群模型

• 批准号: 577225-2022
• 负责人: Hezaveh, YasharY
• 金额: $ 3.28万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763380
• 关键词: Weighing; stars; Data; driven; stellar

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.

由于新一代巡天（例如欧几里得、鲁宾/LSST等），预计很快就会获得大量天体物理数据，为回答有关暗物质和星系形成的关键问题提供了前所未有的机会。这些调查将产生超过 200,000 个强引力透镜，比当前样本大两个数量级。这种透镜是星系中暗物质的理想示踪剂。该项目旨在开发和利用机器学习方法来执行透镜星系的恒星合成建模。恒星种群和强透镜建模的结合将使我们能够分离各个系统中透镜的恒星和暗物质成分，并通过分层推理框架，确定星系的暗物质和恒星成分的群体水平属性，提供独特的暗物质模型和星系形成情景的限制。引力透镜通过透镜星系在遥远来源的图像中引起的引力扭曲来追踪物质（暗物质和重子的组合）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测试暗物质模型预测和理解星系形成和演化中重子物理过程的关键。这是通过恒星合成建模来完成的，该过程将模型恒星光谱组合起来以拟合一组宽带图像。虽然这些模型由于各种假设和简并性而仍然存在偏差和不确定性，但最近的研究表明，通过将恒星合成的单独测量与强透镜和弱透镜相结合，分别探测星系的内部和外部，可以发现大量的恒星合成。借助强透镜系统，可以对暗物质特性和恒星成分进行统计推断的无偏测量，从而可以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大量数据（甚至当前可用的数据）执行此练习是很困难的。然而，最近的进展表明，机器学习可以将镜头建模过程加速超过 1000 万倍，从而可以在几分钟内解决计算上棘手的镜头建模问题。我们的团队目前正在构建方法和管道来做到这一点。我们将通过获得恒星质量并建立一个分层推理框架来扩展这些努力，该框架将星系恒星种群的测量和透镜参数与无与伦比大小的样本结合起来，以便直接探测重子质量分数、内/外层质量分数的时间演化。星系质量比，以及质量吸积的环境依赖性。在预期的全天巡天中，欧几里得天文台和鲁宾天文台预计将于 2023 年和 2024 年开始运行，这使得该项目最为及时。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化的独特特征，这是我们项目的必要输入。我们的跨学科团队由强弱引力透镜分析、恒星种群建模和机器学习方面的专家组成，为这个令人兴奋的项目的各个方面形成了独特的专家合作。