From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses

TL;DR

This paper develops an end-to-end neural inference method to analyze strong gravitational lensing images, enabling efficient constraints on small-scale dark matter structures from large datasets, which is crucial for testing dark matter theories.

Contribution

It introduces a simulation-based neural inference pipeline that accurately estimates the subhalo mass function from complex lensing data, scalable to hundreds of lenses.

Findings

Neural network reliably infers the subhalo mass function.

Method scales efficiently to large lens populations.

Framework is suitable for future wide-field surveys.

Abstract

Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos ($<10^{10} M_\odot$) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated datasets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.