17 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…
The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates
Dhvani Doshi, Nicolas B. Cowan, Ãtienne Artigau +4
Precise radial velocity (pRV) measurements of M dwarfs in the near-infrared (NIR) rely on empirical templates due to the lack of accurate stellar spectral models in this regime. Te…
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…