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Learning the Evolution of the Universe in N-body Simulations
Chang Chen, Yin Li, Francisco Villaescusa-Navarro +2
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have bee…
Fast and Accurate Non-Linear Predictions of Universes with Deep Learning
Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro +2
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 compa…
dm2gal: Mapping Dark Matter to Galaxies with Neural Networks
Noah Kasmanoff, Francisco Villaescusa-Navarro, Jeremy Tinker +1
Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quan…
Modeling assembly bias with machine learning and symbolic regression
Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho +1
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over unprecedented volumes. Mock catalogues are needed to fully exploit the potential of the…
Neural networks as optimal estimators to marginalize over baryonic effects
Francisco Villaescusa-Navarro, Benjamin D. Wandelt, Daniel Anglés-Alcázar +4
Many different studies have shown that a wealth of cosmological information resides on small, non-linear scales. Unfortunately, there are two challenges to overcome to utilize that…
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
V. Ashley Villar, Miles Cranmer, Gabriella Contardo +2
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to fla…