Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies

TL;DR

This paper introduces advanced conditional generative models, including diffusion and flow-based methods, to efficiently generate accurate 3D cosmic density fields for testing gravity theories in cosmology.

Contribution

It develops a novel multi-output model for better conditional generation and demonstrates the effectiveness of diffusion models in reproducing key cosmological statistics.

Findings

Diffusion models accurately reproduce matter power spectra and bispectra.

Flow-based models offer speed-up with slightly reduced accuracy.

The proposed models facilitate efficient exploration of gravity deviations.

Abstract

Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-linear cosmic web. We address this challenge by exploring conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods. Our results demonstrate the power of diffusion models to accurately reproduce the matter power spectra and bispectra, even for unseen configurations. They also offer a significant speed-up with slightly reduced accuracy, when flow-based reconstructing the probability distribution function, but they struggle with higher-order statistics. To improve conditional generation, we introduce a novel multi-output model to develop feature representations of the cosmological parameters. Our findings offer a powerful tool for exploring deviations from standard gravity, combining high precision with reduced computational cost, thus paving the way for more comprehensive and efficient cosmological analyses