Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

By J. Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer. 2019

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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. We find that, within our simplified modeling framework, analyzing a sample of around 100 lenses can already pin down the overall abundance of substructure within lensing galaxies to a precision of % with greater sensitivity expected from a larger lens sample. (https://github.com/smsharma/StrongLensing-Inference)

Subfields: Artificial Intelligence, Astronomy and Astrophysics, Nuclear and High Energy Physics

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Citations in the corpus

Constraining Effective Field Theories with Machine Learning. (2018, background): “Reference [24] demonstrates an application outside the ﬁeld of high energy physics.”