Cosmo-FOLD: Fast generation and upscaling of field-level cosmological maps with overlap latent diffusion

2026-01-20

AI Summary

This work proposes Cosmo-FOLD, a novel method that addresses the high computational cost of traditional hydrodynamical cosmological simulations by enabling efficient generation of large-scale, high-fidelity 3D cosmic fields. Leveraging an overlapping latent diffusion model, Cosmo-FOLD accurately upsamples to full-resolution fields using only approximately 1% of the training volume and generalizes across different simulation datasets without fine-tuning. The approach integrates probabilistic diffusion, latent-space modeling, positional encoding, and an overlapping patch strategy to enable efficient 3D field synthesis on a single GPU. Evaluated on the TNG300-2 dataset, the reconstructed dark matter density and gas temperature fields exhibit power spectrum errors below 10% for wavenumbers up to \(k \leq 5 \, h\,\text{Mpc}^{-1}\), while preserving higher-order statistics such as the bispectrum with high fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We demonstrate the capabilities of probabilistic diffusion models to reduce dramatically the computational cost of expensive hydrodynamical simulations to study the relationship between observable baryonic cosmological probes and dark matter at field level and well into the non-linear regime. We introduce a novel technique, Cosmo-FOLD (Cosmological Fields via Overlap Latent Diffusion) to rapidly generate accurate and arbitrarily large cosmological and astrophysical 3-dimensional fields, conditioned on a given input field. We are able to generate TNG300-2 dark matter density and gas temperature fields from a model trained only on ~1% of the volume (a process we refer to as `upscaling'), reproducing both large scale coherent dark matter filaments and power spectra to within 10% for wavenumbers k<= 5 h Mpc^-1. These results are obtained within a small fraction of the original simulation cost and produced on a single GPU. Beyond one and two points statistics, the bispectrum is also faithfully reproduced through the inclusion of positional encodings. Finally, we demonstrate Cosmo-FOLD's generalisation capabilities by upscaling a CAMELS volume of 25 (Mpc h^-1)^3 to a full TNG300-2 volume of 205 (Mpc h^-1)^3$ with no fine-tuning. Cosmo-FOLD opens the door to full field-level simulation-based inference on cosmological scale.

Problem

• cosmological simulations
• field-level inference
• hydrodynamical simulations
• computational cost
• non-linear regime

Innovation

• latent diffusion
• cosmological field generation
• upscaling
• simulation-based inference
• field-level modeling