# Explaining dark matter halo density profiles with neural networks

arXiv: https://arxiv.org/abs/2305.03077 · HTML: https://mlsanity.com/paper/2305.03077/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 81 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 8 of 8)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [Field Level Neural Network Emulator for Cosmological N-body Simulations](https://mlsanity.com/paper/2206.04594/) (2022)
- [Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra](https://mlsanity.com/paper/2009.12872/) (2020)
- [Discovering the building blocks of dark matter halo density profiles with neural networks](https://mlsanity.com/paper/2203.08827/) (2022)
- [DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes](https://mlsanity.com/paper/2203.09616/) (2022)
- [A robust estimator of mutual information for deep learning interpretability](https://mlsanity.com/paper/2211.00024/) (2022)

## Built on by (most cited first, 4 of 4)

- [Interpretable Machine Learning in Physics: A Review](https://mlsanity.com/paper/2503.23616/) (2025)
- [Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics](https://mlsanity.com/paper/2606.26228/) (2026)
- [A representation learning approach to probe for dynamical dark energy in matter power spectra](https://mlsanity.com/paper/2310.10717/) (2023)
- [LambdaCDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum](https://mlsanity.com/paper/2502.09810/) (2025)

## Abstract (arXiv)

We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation, which we physically interpret using mutual information. Without any prior knowledge of the halos' evolution, the network recovers the known relation between the early time assembly and the inner profile, and discovers that the profile beyond the virial radius is described by a single parameter capturing the most recent mass accretion rate. The results illustrate the potential for machine-assisted scientific discovery in complicated astrophysical datasets.