22 citations · 55 across the 19 of their papers we have counts for
19 papers
Observation-driven simulations of strong lensing galaxy clusters
L. Leuzzi, M. Meneghetti, A. Adam +9
Galaxy clusters are the most powerful strong lenses: they greatly magnify the flux of distant and faint sources. Strong lensing also allows for the reconstruction of their mass dis…
Mapping the Information Geometry of an Unresolved Dark Matter Population using a Differentiable Strong Lensing Simulator
Alexandre Adam
Strong gravitational lensing is a unique probe of the matter power spectrum on small scales, where the abundance of dark matter subhalos in galaxies could be used to distinguish th…
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…
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…
The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Eirini Angeloudi, Marc Huertas-Company, Jesús Falcón-Barroso +3
Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constraining its evolution. Spatially resolving the…
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…
Deconvolving X-ray Galaxy Cluster Spectra Using a Recurrent Inference Machine
Carter Rhea, Julie Hlavacek-Larrondo, Alexandre Adam +4
Recent advances in machine learning algorithms have unlocked new insights in observational astronomy by allowing astronomers to probe new frontiers. In this article, we present a m…
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…
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…
Unraveling the Mysteries of Galaxy Clusters: Recurrent Inference Deconvolution of X-ray Spectra
Carter Rhea, Julie Hlavacek-Larrondo, Ralph Kraft +3
In the realm of X-ray spectral analysis, the true nature of spectra has remained elusive, as observed spectra have long been the outcome of convolution between instrumental respons…
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…
The search for the lost attractor
Mario Pasquato, Syphax Haddad, Pierfrancesco Di Cintio +8
N-body systems characterized by inverse square attractive forces may display a self similar collapse known as the gravo-thermal catastrophe. In star clusters, collapse is halted by…
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…