2020/12/01 by Renan Alves de Oliveira, Yin Li, de Oliveira, Renan Alves +7 · 4 citations
Computer Science · Engineering · Physics and Astronomy · #Astronomy and Astrophysical Research #CCD and CMOS Imaging Sensors #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Computer and information sciences #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #astro-ph.CO #astro-ph.IM #cs.LG
paper · pdf · doi:10.48550/arxiv.2012.00240
6 pages, 2 figures, accepted for a poster presentation at Machine Learning and the Physical Sciences Workshop @ NeurIPS 2020
arxiv created 2020/12/01 · openalex publication_date 2020/12/01 · arxiv updated 2021/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.