Super-resolution emulator of cosmological simulations using deep physical models. Issue 4 (23rd May 2020)

• Record Type: Journal Article
• Authors: Kodi Ramanah, Doogesh; Charnock, Tom; Villaescusa-Navarro, Francisco; Wandelt, Benjamin D
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 495:Issue 4(2020)
• Page Start: 4227
• Page End: 4236
• Publication Date: 2020-05-23
• Subjects: methods: numerical -- methods: statistical -- dark matter -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa1428
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We present an extension of our recently developed Wasserstein optimized model to emulate accurate high-resolution (HR) features from computationally cheaper low-resolution (LR) cosmological simulations. Our deep physical modelling technique relies on restricted neural networks to perform a mapping of the distribution of the LR cosmic density field to the space of the HR small-scale structures. We constrain our network using a single triplet of HR initial conditions and the corresponding LR and HR evolved dark matter simulations from the quijote suite of simulations. We exploit the information content of the HR initial conditions as a well-constructed prior distribution from which the network emulates the small-scale structures. Once fitted, our physical model yields emulated HR simulations at low computational cost, while also providing some insights about how the large-scale modes affect the small-scale structure in real space.