TL;DR
This paper presents a neural network emulator that rapidly computes the linear matter power spectrum with high accuracy across a broad parameter space, significantly speeding up large-scale structure data analysis.
Contribution
The authors develop a fast, accurate neural network emulator for the linear matter power spectrum and related fields, trained on extensive simulations, to enhance cosmological data analysis efficiency.
Findings
Emulator computes the linear spectrum in about one millisecond.
Achieves 0.2% to 0.5% accuracy over relevant redshifts and scales.
Enables unbiased cosmological constraints in Euclid-like surveys.
Abstract
The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables. Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires - evaluations, which means this task can be the most computationally expensive aspect of data analysis. Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with 0.2% (0.5%) accuracy over redshifts (), and scales . We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy. We show that the parameter range and accuracy of our…
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