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

点击翻译按钮获取中文摘要

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.