A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks

TL;DR

This paper introduces a fast, noise-free spherical mass map emulator for the KiDS-1000 survey using a conditional GAN and spherical CNN, enabling efficient cosmological analyses with high accuracy.

Contribution

It presents a novel deep learning-based emulator that generates realistic mass maps from cosmological parameters, significantly reducing computational resources needed for map-based cosmological studies.

Findings

Achieves less than 10% discrepancy in key statistical measures

Provides accurate cosmological parameter constraints

Generates maps in a fraction of a second

Abstract

Large sets of matter density simulations are becoming increasingly important in large-scale structure cosmology. Matter power spectra emulators, such as the Euclid Emulator and CosmicEmu, are trained on simulations to correct the non-linear part of the power spectrum. Map-based analyses retrieve additional non-Gaussian information from the density field, whether through human-designed statistics such as peak counts, or via machine learning methods such as convolutional neural networks. The simulations required for these methods are very resource-intensive, both in terms of computing time and storage. Map-level density field emulators, based on deep generative models, have recently been proposed to address these challenges. In this work, we present a novel mass map emulator of the KiDS-1000 survey footprint, which generates noise-free spherical maps in a fraction of a second. It takes a set of cosmological parameters $(\Omega_M, \sigma_8)$ as input and produces a consistent set of 5 maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct the emulator, we use a conditional generative adversarial network architecture and the spherical CNN $\texttt{DeepSphere}$, and train it on N-body-simulated mass maps. We compare its performance using an array of quantitative comparison metrics: angular power spectra $C_\ell$, pixel/peaks distributions, $C_\ell$ correlation matrices, and Structural Similarity Index. Overall, the average agreement on these summary statistics is $<10\%$ for the cosmologies at the centre of the simulation grid, and degrades slightly on grid edges. Finally, we perform a mock cosmological parameter estimation using the emulator and the original simulation set. We find good agreement in these constraints, for both likelihood and likelihood-free approaches. The emulator is available at https://tfhub.dev/cosmo-group-ethz/models/kids-cgan/1.