Investigating cosmological GAN emulators using latent space interpolation

Andrius Tamosiunas,David J Bacon,Ben Mawdsley,Hans A Winther,Robert C Nichol,Kazuya Koyama

doi:10.1093/mnras/stab1879

Abstract

ABSTRACT Generative adversarial networks (GANs) have been recently applied as a novel emulation technique for large-scale structure simulations. Recent results show that GANs can be used as a fast and efficient emulator for producing novel weak lensing convergence maps as well as cosmic web data in 2D and 3D. However, like any algorithm, the GAN approach comes with a set of limitations, such as an unstable training procedure, inherent randomness of the produced outputs, and difficulties when training the algorithm on multiple data sets. In this work, we employ a number of techniques commonly used in the machine learning literature to address the mentioned limitations. Specifically, we train a GAN to produce weak lensing convergence maps and dark matter overdensity field data for multiple redshifts, cosmological parameters, and modified gravity models. In addition, we train a GAN using the newest Illustris data to emulate dark matter, gas, and internal energy distribution data simultaneously. Finally, we apply the technique of latent space interpolation as a tool for understanding the feature space of the GAN algorithm. We show that the latent space interpolation procedure allows the generation of outputs with intermediate cosmological parameters that were not included in the training data. Our results indicate a 1–20 per cent difference between the power spectra of the GAN-produced and the test data samples depending on the data set used and whether Gaussian smoothing was applied. Similarly, the Minkowski functional analysis indicates a good agreement between the emulated and the real images for most of the studied data sets.

Highlights

In the era of precision cosmology an important tool for studying the evolution of large-scale structure is N-body simulations
The diagnostics were computed at an ensemble level – 100 batches of 64 convergence maps were produced by the Generative adversarial networks (GANs) and the mean values along with the standard deviation were computed and compared against the training and the test data sets
The main goal of this work was to investigate whether GANs can be used as a fast and efficient emulator capable of producing realistic and novel mock data

Summary

Introduction

In the era of precision cosmology an important tool for studying the evolution of large-scale structure is N-body simulations. Modern cosmological simulations are highly realistic and extremely complex and may include galaxy evolution, feedback processes, massive neutrinos, weak lensing, and many other effects. Such complexity, comes at a price in terms of computational resources and large simulations may take several days or even weeks to run. To fully account for galaxy formation and other effects various simplification schemes and semi-analytical models are required To address these issues, a variety of emulation techniques have been discussed in the literature (Kwan et al 2015; Knabenhans et al 2019; Winther et al 2019). In light of upcoming surveys like Euclid, such emulators will be an invaluable tool for producing mock data quickly and efficiently

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Conclusion