# Robust marginalization of baryonic effects for cosmological inference at the field level

arXiv: https://arxiv.org/abs/2109.10360 · HTML: https://mlsanity.com/paper/2109.10360/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks](https://mlsanity.com/paper/2011.05991/) (2020)
- [Multifield Cosmology with Artificial Intelligence](https://mlsanity.com/paper/2109.09747/) (2021)

## Built on by (most cited first, 6 of 6)

- [Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation](https://mlsanity.com/paper/2111.08030/) (2021)
- [Field Level Neural Network Emulator for Cosmological N-body Simulations](https://mlsanity.com/paper/2206.04594/) (2022)
- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets](https://mlsanity.com/paper/2311.01588/) (2023)
- [Learnable wavelet neural networks for cosmological inference](https://mlsanity.com/paper/2307.14362/) (2023)
- [Cosmology with Persistent Homology: Parameter Inference via Machine Learning](https://mlsanity.com/paper/2412.15405/) (2024)

## Abstract (arXiv)

We train neural networks to perform likelihood-free inference from (25,h-1rm Mpc)2 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales (gtrsim 100,h-1rm kpc) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of Omegarm m (± 4%) and σ8 (± 2.5%) from simulations completely different to the ones used to train it.