A Gigaparsec-Scale Hydrodynamic Volume Reconstructed with Deep Learning

Kavli Affiliate: Salman Habib

| First 5 Authors: Cooper Jacobus, Solene Chabanier, Peter Harrington, JD Emberson, Zarija Lukić

| Summary:

The next generation of cosmological spectroscopic sky surveys will probe the
distribution of matter across several Gigaparsecs (Gpc) or many billion
light-years. In order to leverage the rich data in these new maps to gain a
better understanding of the physics that shapes the large-scale structure of
the cosmos, observed matter distributions must be compared to simulated mock
skies. Small mock skies can be produced using precise, physics-driven
hydrodynamical simulations. However, the need to capture small, kpc-scale
density fluctuations in the intergalactic medium (IGM) places tight
restrictions on the necessary minimum resolution of these simulations. Even on
the most powerful supercomputers, it is impossible to run simulations of such
high resolution in volumes comparable to what will be probed by future surveys,
due to the vast quantity of data needed to store such a simulation in computer
memory. However, it is possible to represent the essential features of these
high-resolution simulations using orders of magnitude less memory. We present a
hybrid approach that employs a physics-driven hydrodynamical simulation at a
much lower-than-necessary resolution, followed by a data-driven, deep-learning
Enhancement. This hybrid approach allows us to produce hydrodynamic mock skies
that accurately capture small, kpc-scale features in the IGM but which span
hundreds of Megaparsecs. We have produced such a volume which is roughly one
Gigaparsec in diameter and examine its relevant large-scale statistical
features, emphasizing certain properties that could not be captured by previous
smaller simulations. We present this hydrodynamic volume as well as a companion
n-body dark matter simulation and halo catalog which we are making publically
available to the community for use in calibrating data pipelines for upcoming
survey analyses.

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