BibTeX

@article{2512.11687v1,
Author        = {Nolan Smyth and Laurence Perreault-Levasseur and Yashar Hezaveh},
Title         = {Transformer Embeddings for Fast Microlensing Inference},
Eprint        = {2512.11687v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {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 typically detected via short-duration microlensing events, the characterization of which often requires analyzing noisy, irregularly-sampled observations. We present a pipeline for this task using simulation-based inference. We use a Transformer encoder to learn a compressed summary representation of the raw time-series data, which in turn conditions a neural posterior estimator. We demonstrate that our method produces accurate and well-calibrated posteriors over three orders of magnitude faster than traditional methods. We also demonstrate its performance on KMT-BLG-2019-2073, a short-duration FFP candidate event.},
Year          = {2025},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2512.11687v1},
File          = {2512.11687v1.pdf}
}