327 citations · 4.5k across the 98 of their papers we have counts for
20 papers · 1 filter
Improving Gradient-guided Nested Sampling for Posterior Inference
Pablo Lemos, Nikolay Malkin, Will Handley +3
We present a performant, general-purpose gradient-guided nested sampling algorithm, , combining the state of the art in differentiable programming, Hamiltonian slice sa…
Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot, Pablo Lemos, Laurence Perreault-Levasseur +3
Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show ho…
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…
Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem
Nicolas Payot, Mario Pasquato, Alessandro Alberto Trani +2
Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g.…
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…
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato, Piero Trevisan, Abbas Askar +4
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle pred…
Searching for strong gravitational lenses
Cameron Lemon, Frédéric Courbin, Anupreeta More +9
Strong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered - a task that can be met with significa…
Time Delay Cosmography with a Neural Ratio Estimator
Ève Campeau-Poirier, Laurence Perreault-Levasseur, Adam Coogan +1
We explore the use of a Neural Ratio Estimator (NRE) to determine the Hubble constant () in the context of time delay cosmography. Assuming a Singular Isothermal Ellipsoid (SI…
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…
TEMPLATES: Characterization of a Merger in the Dusty Lensing SPT0418-47 System
Jared Cathey, Anthony H. Gonzalez, Sidney Lower +22
We present JWST and ALMA results for the lensing system SPT0418-47, which includes a strongly-lensed, dusty star-forming galaxy at redshift z=4.225 and an associated multiply-image…
Strong gravitational lensing as a probe of dark matter
S. Vegetti, S. Birrer, G. Despali +8
Dark matter structures within strong gravitational lens galaxies and along their line of sight leave a gravitational imprint on the multiple images of lensed sources. Strong gravit…
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…
On Diffusion Modeling for Anomaly Detection
Victor Livernoche, Vineet Jain, Yashar Hezaveh +1
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different var…
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…
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
Pablo Lemos, Adam Coogan, Yashar Hezaveh +1
Parameter inference, i.e. inferring the posterior distribution of the parameters of a statistical model given some data, is a central problem to many scientific disciplines. Genera…
Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at
Ramesh Mainali, Daniel P. Stark, Tucker Jones +3
We present new observations of sixteen bright () gravitationally lensed galaxies at selected from the CASSOWARY survey. Included in our sample is the $z=1.42…
Extended Lyman- emission towards the SPT2349-56 protocluster at
Yordanka Apostolovski, Manuel Aravena, Timo Anguita +20
Context. Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared sources in the sky correspond to…
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…