Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

TL;DR

This paper introduces a machine learning approach using simulation-based inference to analyze dark matter substructure in strong lensing systems, enabling efficient extraction of population properties from complex data.

Contribution

It applies neural network-based likelihood ratio estimation to infer dark matter subhalo populations from strong lensing data, overcoming intractable likelihood challenges.

Findings

Neural networks can estimate likelihood ratios for substructure parameters.

Method efficiently analyzes multiple lenses simultaneously.

Proof-of-principle demonstrated on simulated data.

Abstract

The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics. However, teasing out this effect poses a significant challenge since the likelihood function for realistic simulations of population-level parameters is intractable. We apply recently-developed simulation-based inference techniques to the problem of substructure inference in galaxy-galaxy strong lenses. By leveraging additional information extracted from the simulator, neural networks are efficiently trained to estimate likelihood ratios associated with population-level parameters characterizing substructure. Through proof-of-principle application to simulated data, we show that these methods can provide an efficient and principled way to simultaneously analyze an ensemble of strong lenses, and can be used to mine the large sample of lensing images deliverable by near-future surveys for signatures of dark matter substructure.