Simulation-based inference from the Lyman-alpha forest 1D power spectrum with CAMELS

TL;DR

This paper demonstrates the first full simulation-based inference approach for the Lyman-alpha forest 1D power spectrum using neural networks, enabling improved cosmological parameter estimation from hydrodynamic simulations.

Contribution

It introduces a neural posterior estimation method with normalizing flows for the Lyman-alpha forest, addressing model dependence and combining multiple galaxy formation models for unbiased constraints.

Findings

Accurate parameter recovery within 10% when training and testing on the same model.

Significant performance drop when training and testing on different models.

Multi-domain training recovers unbiased cosmological constraints.

Abstract

We perform for the first time full simulation-based inference on the Lyman-$\alpha$ forest 1D power spectrum. In particular, we consider the prediction of the Lyman-$\alpha$ forest $P_{\rm 1D}(k)$ at $2.0<z<3.5$ from the CAMELS cosmological hydrodynamic simulations run with the IllustrisTNG and SIMBA galaxy formation models. We train a normalizing flow to perform neural posterior estimation of two cosmological parameters ($\Omega_m$ and $\sigma_8$) and four astrophysical parameters parametrizing supernova and AGN feedback. When training and testing the neural network on the same baryon physics model, the posterior distributions of the cosmological parameters are found to be in excellent agreement with the true parameters values (within $10\%$ deviations in $\gtrsim 75\%$ and $\gtrsim 90\%$ of the cases for $\Omega_m$ and $\sigma_8$, and a precision better than $10\%$ in both), while the astrophysical parameters are generally unconstrained due to the limited probed volume. When training on one model and testing on the other (e.g., training on IllustrisTNG and testing on SIMBA, or viceversa), the performance is significantly worse, both in accuracy and in precision, resulting in a $\sim 10\%$ positive bias on the predicted values for $\sigma_8$. We show that a multi-domain training based on the combination of simulations from both models recovers unbiased constraints, offering an effective solution to cope with the complex problem of the lack of convergence in the predictions from different galaxy formation models. This study represents a promising way forward to constrain cosmology and fundamental physics with the Lyman-$\alpha$ forest with artificial intelligence.