Fast cosmic web simulations with generative adversarial networks

Abstract

Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, such as classical N-body simulations, are extremely resource intensive, as they track the action of gravity in an expanding universe using billions of particles as tracers of the cosmic matter distribution. Therefore, upcoming cosmology experiments will face a computational bottleneck that may limit the exploitation of their full scientific potential. To address this challenge, we demonstrate the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web. Our training set is a small, representative sample of 2D image snapshots from N-body simulations of size 500 and 100 Mpc. We show that the GAN-generated samples are qualitatively and quantitatively very similar to the originals. For the larger boxes of size 500 Mpc, it is very difficult to distinguish them visually. The agreement of the power spectrum $P_k$ is 1-2\% for most of the range, between $k=0.06$ and $k=0.4$. An important advantage of generating cosmic web realizations with a GAN is the considerable gains in terms of computation time. Each new sample generated by a GAN takes a fraction of a second, compared to the many hours needed by traditional N-body techniques. We anticipate that the use of generative models such as GANs will therefore play an important role in providing extremely fast and precise simulations of cosmic web in the era of large cosmological surveys, such as Euclid and Large Synoptic Survey Telescope (LSST).

References in corpus (7)

• KiDS-450: Cosmological Constraints from Weak Lensing Peak Statistics - II: Inference from Shear Peaks using N-body Simulations
• A filament of dark matter between two clusters of galaxies
• Full-Sky Weak Lensing Simulation with 70 Billion Particles
• Non-Gaussian information from weak lensing data via deep learning
• Simulations of Weak Gravitational Lensing - II : Including Finite Support Effects in Cosmic Shear Covariance Matrices
• A new model to predict weak-lensing peak counts I. Comparison with $N$-body Simulations
• Fast Generation of Covariance Matrices for Weak Lensing

Cited by in corpus (54)

• Machine learning and the physical sciences
• Large-scale dark matter simulations
• Cosmological constraints with deep learning from KiDS-450 weak lensing maps
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Weak lensing cosmology with convolutional neural networks on noisy data
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• AI-assisted super-resolution cosmological simulations
• Painting with baryons: augmenting N-body simulations with gas using deep generative models
• Applications and Techniques for Fast Machine Learning in Science
• Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy
• Super-resolution emulator of cosmological simulations using deep physical models
• Learning to Denoise Astronomical Images with U-nets
• On the dissection of degenerate cosmologies with machine learning
• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• Painting halos from cosmic density fields of dark matter with physically motivated neural networks
• A deep learning model to emulate simulations of cosmic reionization
• AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
• Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
• Baryon acoustic oscillations reconstruction using convolutional neural networks
• A single $T$-gate makes distribution learning hard
• Nonlinear 3D Cosmic Web Simulation with Heavy-Tailed Generative Adversarial Networks
• Observational cosmology with Artificial Neural Networks
• Extracting cosmological parameters from N-body simulations using machine learning techniques
• Encoding large scale cosmological structure with Generative Adversarial Networks
• Cosmic Velocity Field Reconstruction Using AI
• A black box for dark sector physics: Predicting dark matter annihilation feedback with conditional GANs
• Inpainting Galactic Foreground Intensity and Polarization maps using Convolutional Neural Network
• (DarkAI) Mapping the large-scale density field of dark matter using artificial intelligence
• Investigating Cosmological GAN Emulators Using Latent Space Interpolation
• Deep learning for Sunyaev-Zel'dovich detection in Planck
• Fast and realistic large-scale structure from machine-learning-augmented random field simulations
• Hybrid analytic and machine-learned baryonic property insertion into galactic dark matter haloes
• Optimizing simulation parameters for weak lensing analyses involving non-Gaussian observables
• Probabilistic cosmic web classification using fast-generated training data
• The Sejong Suite: Cosmological Hydrodynamical Simulations with Massive Neutrinos, Dark Radiation, and Warm Dark Matter
• AI-assisted super-resolution cosmological simulations III: Time evolution
• Learning neutrino effects in Cosmology with Convolutional Neural Networks
• Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
• Uncertainties associated with GAN-generated datasets in high energy physics
• Foreground Removal of CO Intensity Mapping Using Deep Learning
• A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks
• Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
• Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
• A Machine Learning Approach to Enhancing eROSITA Observations
• From few to many maps: A fast map-level emulator for extreme augmentation of CMB systematics datasets
• kSZ Pairwise Velocity Reconstruction with Machine Learning
• Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks
• Fast Generation of Large-scale Structure Density Maps via Generative Adversarial Networks
• Power Spectrum Emulators from Neural Networks and Tree-Based Methods
• Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods
• DeepVoid: A Deep Learning Void Detector
• Replicating weak-lensing summary-statistic covariances with normalizing flows
• nuGAN: Generative Adversarial Emulator for Cosmic Web with Neutrinos
• Generative modeling of convergence maps based on predicted one-point statistics