Towards Robustness Across Cosmological Simulation Models TNG, SIMBA, ASTRID, and EAGLE

TL;DR

This paper introduces MIEST, a machine learning estimator that robustly predicts cosmological parameters across diverse simulation models, enhancing reliability and physical insight in cosmological research.

Contribution

We develop a novel model-insensitive estimator that accurately predicts cosmological parameters across multiple simulation models, reducing bias and improving robustness.

Findings

MIEST improves parameter estimation accuracy by ~17% for Ω_m and ~38% for σ_8.

Latent space analysis shows blending of features across models, indicating removal of model-specific biases.

Robustness enhances the generalization of cosmological parameter estimation across different simulation frameworks.

Abstract

The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmological simulation models presents a challenge to the robustness. In this work, we develop the Model-Insensitive ESTimator (MIEST), a machine that can robustly estimate the cosmological parameters, $\Omega_m$ and $\sigma_8$, from neural hydrogen maps of simulation models in the CAMELS project$-$TNG, SIMBA, ASTRID, and EAGLE. An estimator is considered robust if it possesses a consistent predictive power across all simulations, including those used during the training phase. We train our machine using multiple simulation models and ensure that it only extracts common features between the models while disregarding the model-specific features. This allows us to develop a novel model that is capable of accurately estimating parameters across a range of simulation models, without being biased towards any particular model. Upon the investigation of the latent space$-$a set of summary statistics, we find that the implementation of robustness leads to the blending of latent variables across different models, demonstrating the removal of model-specific features. In comparison to a standard machine lacking robustness, the average performance of MIEST on the unseen simulations during the training phase has been improved by $\sim17$% for $\Omega_m$ and $\sim 38$% for $\sigma_8$. By using a machine learning approach that can extract robust, yet physical features, we hope to improve our understanding of galaxy formation and evolution in a (subgrid) model-insensitive manner, and ultimately, gain insight into the underlying physical processes responsible for robustness. This is a Learning the Universe publication.