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.
@article{arxiv.2512.11687,
title = {Transformer Embeddings for Fast Microlensing Inference},
author = {Nolan Smyth and Laurence Perreault-Levasseur and Yashar Hezaveh},
journal= {arXiv preprint arXiv:2512.11687},
year = {2025}
}