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Reconstructing Lyα Fields from Low-resolution Hydrodynamical Simulations with Deep Learning
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AI
DOI：10.3847/1538-4357/acfcb5.png)
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摘要
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Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly alpha flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly alpha flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly alpha mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.
Keyword:
BARYON ACOUSTIC-OSCILLATIONS
FOREST POWER SPECTRUM
SMALL-SCALE STRUCTURE
DARK-MATTER
INTERGALACTIC MEDIUM
NEURAL-NETWORKS
CONSTRAINTS
MASS
IGM
REIONIZATION
期刊
IF:
5.4
论文数:
8.3W
被引数:
32.0W
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