22 citations · 55 across the 19 of their papers we have counts for
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Probabilistic Interpolation of Sagittarius A*'s Multi-Wavelength Light Curves Using Diffusion Models
Gabriel Sasseville, Julie Hlavacek-Larrondo, Daryl Haggard +3
Understanding the variability of Sagittarius A* (Sgr A*) requires coordinated, multi-wavelength observations that span the electromagnetic spectrum. In this work, we focus on data…
Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images
Connor Stone, Ronan Legin, Alexandre Adam +4
We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF characterization is critical for precision meas…
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
Noé Dia, M. J. Yantovski-Barth, Alexandre Adam +4
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we i…
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…
Bayesian Imaging for Radio Interferometry with Score-Based Priors
Noe Dia, M. J. Yantovski-Barth, Alexandre Adam +5
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner. We use a sc…
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Alexandre Adam, Connor Stone, Connor Bottrell +3
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the non-t…
Beyond Gaussian Noise: A Generalized Approach to Likelihood Analysis with non-Gaussian Noise
Ronan Legin, Alexandre Adam, Yashar Hezaveh +1
Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function of complex, high-dimensional, non-Gauss…
Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines
Alexandre Adam, Laurence Perreault-Levasseur, Yashar Hezaveh +1
Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has been…
Posterior samples of source galaxies in strong gravitational lenses with score-based priors
Alexandre Adam, Adam Coogan, Nikolay Malkin +4
Inferring accurate posteriors for high-dimensional representations of the brightness of gravitationally-lensed sources is a major challenge, in part due to the difficulties of accu…
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machines
Alexandre Adam, Laurence Perreault-Levasseur, Yashar Hezaveh
Modeling strong gravitational lenses in order to quantify the distortions in the images of background sources and to reconstruct the mass density in the foreground lenses has tradi…