Metraipolis › archive › dc_7dced4f459d3

Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves — arXiv:2608.19419

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19419
License (as archived): CC0-1.0
Archived: 2026-10-18T11:48:38.049Z
SHA-256 of the archived text: 5df9172b58894e6ce1f766a067624bd4a249ef6314e23258089fdc1c1fa44fc5
Stable URL of this page: https://metraipolis.com/documento/dc_7dced4f459d3

How to cite

Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves — arXiv:2608.19419 — arXiv (en), https://arxiv.org/abs/2608.19419 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7dced4f459d3 (Metraipolis archive dc_7dced4f459d3, sha256 5df9172b58894e6ce1f766a067624bd4a249ef6314e23258089fdc1c1fa44fc5)

BibTeX

@misc{dc7dced4f459d3,
  title = {Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves — arXiv:2608.19419},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19419},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7dced4f459d3; sha256 5df9172b58894e6ce1f766a067624bd4a249ef6314e23258089fdc1c1fa44fc5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19419. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for these objects across the sky. The Transiting Exoplanet Survey Satellite (TESS), primarily designed to detect transiting exoplanets, also provides near all-sky coverage with high cadence. In this work, we use TESS data to search for microlensing candidates using both traditional and machine-learning methods and to identify associated false positives in high-cadence surveys. Microlensify is a physics-informed, transformer-based variational autoencoder trained on simulated single-lens microlensing light curves and real TESS Sector 12 data. The model classifies events, reconstructs light curves, and estimates microlensing event durations. Applied to $\sim 5.6$ million TESS light curves, it identified between $0.036\%$ and $1.89\%$ as microlensing candidates across different TESS pipelines. After applying microlensing detection metrics and cross-matching with SIMBAD, we obtained a final list of candidates and identified false positives including long-period variables, Mira variables, cataclysmic variables, red giants, and transients. We also found Gaussian-like peaks caused by asteroid crossings, a potential source of false positives in high-cadence microlensing surveys. The model also predicts event duration with an accuracy of $R^2 = 0.97$. The model was further tested on published events from different ground-based microlensing surveys, confirming 92.7% as microlensing, demonstrating its applicability across surveys with different cadences.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.LG).
Categorie arXiv: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; Earth and Planetary Astrophysics (astro-ph.EP); Astrophysics of Galaxies (astro-ph.GA); Solar and Stellar Astrophysics (astro-ph.SR); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.19419 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:47:43.363Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).