  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.
