Transformer Embeddings for Fast Microlensing Inference
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915671431970816 |
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| author | Smyth, Nolan Perreault-Levasseur, Laurence Hezaveh, Yashar |
| author_facet | Smyth, Nolan Perreault-Levasseur, Laurence Hezaveh, Yashar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer Embeddings for Fast Microlensing Inference Smyth, Nolan Perreault-Levasseur, Laurence Hezaveh, Yashar Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies 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. |
| title | Transformer Embeddings for Fast Microlensing Inference |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2512.11687 |