Bayesian Deep Gaussian Processes Applied to Cosmological Matter Power Spectra Prediction
Researchers have developed a novel Bayesian deep Gaussian process (DGP) hierarchical model to more accurately predict matter power spectra — a key measure of how matter is distributed across the universe. The work is motivated by the Mira-Titan Universe simulation suite, which generates multiple correlated response curves at varying fidelities across different cosmological parameter settings. The method outperforms the existing benchmark emulator (Cosmic Emu) and offers improved uncertainty quantification, which is critical as cosmological surveys grow in scale and complexity.
A team of researchers has proposed a Bayesian deep Gaussian process hierarchical model designed to estimate matter power spectra from correlated, multi-fidelity simulation data. The model extends prior work on Bayesian DGPs — which previously handled only scalar outputs — to correlated functional outputs, making it better suited to the structure of cosmological simulation data. Using the Mira-Titan Universe simulation suite as a case study, the method synthesizes information across multiple simulation fidelities to produce estimates with rigorous uncertainty quantification. In a second stage, the predicted spectra are represented using basis functions and fed into a separate Gaussian process emulator to predict power spectra for cosmological parameter settings not included in the training data. The approach was validated through synthetic exercises and benchmarked against Cosmic Emu, the standard cosmological emulator, with favorable results. The paper, spanning 22 pages and 14 figures, has been revised and accepted for publication in Data Science in Science.
What's missing
The paper does not detail computational cost or scalability of the proposed DGP model relative to Cosmic Emu, which is relevant for practical deployment in large survey pipelines.
What different sources said
- arXiv astro-phCenter
Bayesian Deep Gaussian Processes for Correlated Functional Data: A Case Study in Cosmological Matter Power Spectra
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