Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
This work addresses the problem of extracting dark matter substructure information from strong lensing data for astrophysicists and cosmologists, representing an incremental advance by applying existing simulation-based inference techniques to a specific domain.
The paper tackles the challenge of inferring dark matter substructure properties from strong lensing images, which is difficult due to intractable likelihood functions, by applying simulation-based inference with neural networks to efficiently estimate likelihood ratios for population-level parameters. Through proof-of-principle tests on simulated data, it demonstrates that this method can enable simultaneous analysis of lens ensembles and mine future survey data for substructure signatures.
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.