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

arXiv: https://arxiv.org/abs/1909.02005 · HTML: https://mlsanity.com/paper/1909.02005/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2019.

## Built on by (most cited first, 15 of 15)

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- [Inferring dark matter substructure with astrometric lensing beyond the power spectrum](https://mlsanity.com/paper/2110.01620/) (2021)
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- [Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning](https://mlsanity.com/paper/2203.08161/) (2022)
- [Strong Gravitational Lensing Parameter Estimation with Vision Transformer](https://mlsanity.com/paper/2210.04143/) (2022)
- [Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution](https://mlsanity.com/paper/2512.06642/) (2025)
- [Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimation](https://mlsanity.com/paper/2607.17540/) (2026)
- [Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervision](https://mlsanity.com/paper/2609.39525/) (2026)

## Abstract (arXiv)

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.