24 citations · 31 across the 3 of their papers we have counts for
5 papers
The LSST-DESC 3x2pt Tomography Optimization Challenge
Joe Zuntz, François Lanusse, Alex I. Malz +25
This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tom…
Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation
Keming Zhang, Joshua S. Bloom, B. Scott Gaudi +3
Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain Monte Carlo; MCMC) is challenged o…
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…