327 citations · 4.5k across the 98 of their papers we have counts for
9 papers · 1 filter
Robustness of Neural Ratio and Posterior Estimators to Distributional Shifts for Population-Level Dark Matter Analysis in Strong Gravitational Lensing
Andreas Filipp, Yashar Hezaveh, Laurence Perreault-Levasseur
We investigate the robustness of Neural Ratio Estimators (NREs) and Neural Posterior Estimators (NPEs) to distributional shifts in the context of measuring the abundance of dark ma…
Gravitational-Wave Parameter Estimation in non-Gaussian noise using Score-Based Likelihood Characterization
Ronan Legin, Maximiliano Isi, Kaze W. K. Wong +2
Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled o…
Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis
Zehao Jin, Mario Pasquato, Benjamin L. Davis +2
We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first ti…
Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution
Zehao Jin, Mario Pasquato, Benjamin L. Davis +9
Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) w…
Inpainting Galaxy Counts onto N-Body Simulations over Multiple Cosmologies and Astrophysics
Antoine Bourdin, Ronan Legin, Matthew Ho +3
Cosmological hydrodynamical simulations, while the current state-of-the art methodology for generating theoretical predictions for the large scale structures of the Universe, are a…
Variable Star Light Curves in Koopman Space
Nicolas Mekhaël, Mario Pasquato, Gaia Carenini +4
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variabl…
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems
Gabriel Missael Barco, Alexandre Adam, Connor Stone +2
Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-level distributions can serve as effecti…
Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations
Connor Stone, Alexandre Adam, Adam Coogan +9
Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any…
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos, Sammy Sharief, Esmeralda S. Whitammer +4
We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQM…