27 citations · 68 across the 10 of their papers we have counts for
3 papers · 1 filter
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
Francois Lanusse, Peter Melchior, Fred Moolekamp
We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model…
CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST
Danila Korytov, Andrew Hearin, Eve Kovacs +27
This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…