1.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de MontréalCA125 papers
- McGill UniversityCA59 papers
- Polytechnique MontréalCA27 papers
- Flatiron Health (United States)US24 papers
- Flatiron Institute23 papers
- Centre National de la Recherche ScientifiqueFR22 papers
- University of TorontoCA16 papers
- Canadian Institute for Advanced ResearchCA14 papers
- Université Paris-SaclayFR14 papers
- University of AmsterdamNL13 papers
- University of WaterlooCA13 papers
- Centre for Research in Astrophysics of QuébecCA12 papers
11 papers · 1 filter
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
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…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…
AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images
Connor Stone, Stephane Courteau, Jean-Charles Cuillandre +3
We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automatic differentiation and GPU (or parallel C…
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…
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…
Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
Guillaume Payeur, Étienne Artigau, Laurence Perreault-Levasseur +1
We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using HxRG detectors in low-flux regimes. It im…
Efficient Gravitational Wave Template Bank Generation with Differentiable Waveforms
Adam Coogan, Thomas D. P. Edwards, Horng Sheng Chia +6
The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational…
Deep Generative Models for Galaxy Image Simulations
Francois Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh +3
Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for t…
Cleaning our own Dust: Simulating and Separating Galactic Dust Foregrounds with Neural Networks
K. Aylor, M. Haq, L. Knox +2
Separating galactic foreground emission from maps of the cosmic microwave background (CMB), and quantifying the uncertainty in the CMB maps due to errors in foreground separation a…