66 citations · 128 across the 14 of their papers we have counts for
14 papers
Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis
Ronan Legin, Connor Stone, Alexandre Adam +5
We present new state-of-the-art lens models for strong gravitational lensing systems from the Sloan Lens ACS (SLACS) survey, developed within a Bayesian framework that employs high…
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…
Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
Gabriel Missael Barco, Ronan Legin, Connor Stone +2
Score-based models can serve as expressive, data-driven priors for scientific inverse problems. In strong gravitational lensing, they enable posterior inference of a background gal…
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…
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…
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…
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…
Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy
Justin S. Spilker, Kedar A. Phadke, Manuel Aravena +35
Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths. Polycyclic aromatic hydrocarbons…
Posterior Sampling of the Initial Conditions of the Universe from Non-linear Large Scale Structures using Score-Based Generative Models
Ronan Legin, Matthew Ho, Pablo Lemos +4
Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer…
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…
A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods using Density Estimation
Ronan Legin, Yashar Hezaveh, Laurence Perreault-Levasseur +1
We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural networks. This allows us to perform deep…
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…
Population-Level Inference of Strong Gravitational Lenses with Neural Network-Based Selection Correction
Ronan Legin, Connor Stone, Yashar Hezaveh +1
A new generation of sky surveys is poised to provide unprecedented volumes of data containing hundreds of thousands of new strong lensing systems in the coming years. Convolutional…
Simulation-Based Inference of Strong Gravitational Lensing Parameters
Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur +1
In the coming years, a new generation of sky surveys, in particular, Euclid Space Telescope (2022), and the Rubin Observatory's Legacy Survey of Space and Time (LSST, 2023) will di…