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

TL;DR

This paper introduces a scalable simulation-based inference method using neural ratio estimation to constrain dark energy parameters from large populations of strong gravitational lens images, improving efficiency over traditional methods.

Contribution

The authors develop a machine learning approach that leverages simulated lens data for population-level dark energy inference, enabling analysis of thousands of lenses efficiently.

Findings

Constrained the dark energy equation-of-state parameter $w$ within $1\sigma$ using simulated data.

Demonstrated scalability to analyze large lens samples from future surveys.

Provided a framework for rapid cosmological inference from upcoming strong lens datasets.

Abstract

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.