Fast and Accurate Non-Linear Predictions of Universes with Deep Learning

Abstract

Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compare simulations of this structure formation process with observations of large-scale structure traced by galaxies and infer the properties of the dark energy and dark matter that make up 95% of the universe. These ensembles of simulations of billions of galaxies are computationally demanding, so that more efficient approaches to tracing the non-linear growth of structure are needed. We build a V-Net based model that transforms fast linear predictions into fully nonlinear predictions from numerical simulations. Our NN model learns to emulate the simulations down to small scales and is both faster and more accurate than the current state-of-the-art approximate methods. It also achieves comparable accuracy when tested on universes of significantly different cosmological parameters from the one used in training. This suggests that our model generalizes well beyond our training set.

arXiv comment6 pages, 2 figures, accepted for a poster presentation at Machine Learning and the Physical Sciences Workshop @ NeurIPS 2020

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• $\mathrm{S}\mathrm{IM}\mathrm{BIG}$: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering
• Machine Learning and Cosmology
• On the primordial information available to galaxy redshift surveys
• Efficient exploration and calibration of a semi-analytical model of galaxy formation with deep learning
• Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
• Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts
• Predicting halo occupation and galaxy assembly bias with machine learning
• Improving cosmological covariance matrices with machine learning