Portrait de Nolan Smyth

Nolan Smyth

nolan.smyth@mila.quebec
Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Yashar Hezaveh
Co-supervisor
Laurence Perreault-Levasseur
Sujets de recherche
Apprentissage automatique appliqué
Astrophysique
Sécurité de l'IA
Site web
Google Scholar
Bluesky
GitHub

Publications

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 determinist… (voir plus)ic hard cuts on photometric statistics, systematically missing low-magnification events in the finite-source regime. We instead frame detection as Bayesian model comparison using Evidence Networks, which learn calibrated Bayes factors from binary-labeled simulations, and combine this with Neural Posterior Estimation (NPE) for amortized parameter inference. Both share a transformer encoder that handles irregularly-sampled time series without imputation. On simulated Roman Space Telescope data, our Evidence Network achieves
2026-05-29
AI4Physics @ International Conference on Machine Learning (publié)
openreview.net
openreview.net
Transformer Embeddings for Fast Microlensing Inference
Nolan Smyth
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-12-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Neural Deprojection of Galaxy Stellar Mass Profiles
M. J. Yantovski-Barth
Hengyue Zhang
Nolan Smyth
Connor Stone
Martin Bureau
Yashar Hezaveh
Laurence Perreault-Levasseur
We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (voir plus) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.
2025-11-24
arXiv (prépublication)
doi.org
arxiv.org