标题： 基于模拟的推断的强引力透镜效应对宇宙学暗能量的约束 显示英文标题

标题： Population-level Dark Energy Constraints from Strong Gravitational Lensing using Simulation-Based Inference

摘要： 在本工作中，我们提出了一种可扩展的方法，通过使用基于仿真的推断（SBI）从强引力透镜图像的群体中推断暗能量状态方程参数（$w$）。 强引力透镜为宇宙学提供了关键见解，但传统的宇宙学推断蒙特卡洛方法计算成本高昂，且无法处理未来宇宙调查预计出现的数千个透镜。 新的推断工具，如使用神经比值估计（NRE）的SBI，有效地解决了这一挑战。 通过在强透镜的模拟数据上训练机器学习模型，我们可以学习似然比证据以实现稳健的推断。 我们的可扩展方法相比单个透镜分析，能够对$w$进行更严格的群体层面推断，将$w$约束在$1\sigma$之内。 我们的模型可用于提供来自即将进行的强透镜调查的宇宙学约束，例如4MOST强透镜光谱遗产调查（4SLSLS），该调查预计将观测到10,000个强透镜。 显示英文摘要

摘要： In this work, we present a scalable approach for inferring the dark energy equation-of-state parameter ($w$) from a population of strong gravitational lens images using Simulation-Based Inference (SBI). Strong gravitational lensing offers crucial insights into cosmology, but traditional Monte Carlo methods for cosmological inference are computationally prohibitive and inadequate for processing the thousands of lenses anticipated from future cosmic surveys. New tools for inference, such as SBI using Neural Ratio Estimation (NRE), address this challenge effectively. By training a machine learning model on simulated data of strong lenses, we can learn the likelihood-to-evidence ratio for robust inference. Our scalable approach enables more constrained population-level inference of $w$ compared to individual lens analysis, constraining $w$ to within $1\sigma$. Our model can be used to provide cosmological constraints from forthcoming strong lens surveys, such as the 4MOST Strong Lensing Spectroscopic Legacy Survey (4SLSLS), which is expected to observe 10,000 strong lenses.