15 papers
Microlensing Detection and Inference via Learned Bayes Factors
Nolan Smyth, Laurence Perreault-Levasseur, Yashar Hezaveh
We present a unified framework for gravitational microlensing event detection and parameter inference. Traditional pipelines use deterministic hard cuts on photometric statistics,…
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
Guillaume Payeur, Laurence Perreault-Levasseur, Gabriel Missael Barco +1
Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is computationally challenging, p…
MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
Sammy Sharief, Justine Zeghal, Gabriel Missael Barco +3
We introduce Mira, a sample-based score for assessing the accuracy of a candidate conditional distribution using only joint samples from the true data-generating process. Relying o…
Transformer Embeddings for Fast Microlensing Inference
Nolan Smyth, Laurence Perreault-Levasseur, Yashar Hezaveh
The search for free-floating planets (FFPs) is a key science driver for upcoming microlensing surveys like the Nancy Grace Roman Galactic Exoplanet Survey. These rogue worlds are t…
Neural Deprojection of Galaxy Stellar Mass Profiles
M. J. Yantovski-Barth, Hengyue Zhang, Nolan Smyth +4
We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable mapping. Specifically, we train a neura…
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…