# Multifield Cosmology with Artificial Intelligence

arXiv: https://arxiv.org/abs/2109.09747 · HTML: https://mlsanity.com/paper/2109.09747/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Deep Contrast Learning for Salient Object Detection](https://mlsanity.com/paper/1603.01976/) (2016)
- [Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks](https://mlsanity.com/paper/2011.05991/) (2020)
- [Estimating Cosmological Parameters from the Dark Matter Distribution](https://mlsanity.com/paper/1711.02033/) (2017)
- [Non-Gaussian information from weak lensing data via deep learning](https://mlsanity.com/paper/1802.01212/) (2018)
- [Cosmological model discrimination with Deep Learning](https://mlsanity.com/paper/1707.05167/) (2017)

## Built on by (most cited first, 12 of 12)

- [Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation](https://mlsanity.com/paper/2111.08030/) (2021)
- [Multiscale Flow for Robust and Optimal Cosmological Analysis](https://mlsanity.com/paper/2306.04689/) (2023)
- [Robust marginalization of baryonic effects for cosmological inference at the field level](https://mlsanity.com/paper/2109.10360/) (2021)
- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [Machine Learning and Cosmology](https://mlsanity.com/paper/2203.08056/) (2022)
- [Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects](https://mlsanity.com/paper/2310.15234/) (2023)
- [Cosmological Field Emulation and Parameter Inference with Diffusion Models](https://mlsanity.com/paper/2312.07534/) (2023)
- [FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology](https://mlsanity.com/paper/2604.14451/) (2026)
- [Learnable wavelet neural networks for cosmological inference](https://mlsanity.com/paper/2307.14362/) (2023)
- [Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations](https://mlsanity.com/paper/2311.18007/) (2023)
- [Cosmology with Persistent Homology: Parameter Inference via Machine Learning](https://mlsanity.com/paper/2412.15405/) (2024)
- [CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching](https://mlsanity.com/paper/2507.11842/) (2025)

## Abstract (arXiv)

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of (25~h-1rm Mpc)2, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of Omegarm m and σ8 with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of Omegarm m with higher accuracy than the maps from the N-body simulations.