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

• URL: http://arxiv.org/abs/2502.17087v1
• Date: Mon, 24 Feb 2025 12:06:23 GMT
• Title: Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies
• Authors: Julieth Katherine Riveros, Paola Saavedra, Hector J. Hortua, Jorge Enrique Garcia-Farieta, Ivan Olier,
• Abstract summary: Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales.<n>We explore conditional generative modelling to create 3D dark matter density fields via score-based (diffusion) and flow-based methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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