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arXiv Machine Learning By Phil R. Van-Lane (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Department of Astronomy and Astrophysics, University of California San Diego), Joshua S. Speagle (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Department of Statistical Sciences, University of Toronto, Dunlap Institute for Astronomy and Astrophysics, University of Toronto, Data Sciences Institute, University of Toronto), Ryan Cloutier (Department of Physics and Astronomy, McMaster University), Christopher A. Theissen (Department of Astronomy and Astrophysics, University of California San Diego), Gwendolyn M. Eadie (David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto, Department of Statistical Sciences, University of Toronto, Data Sciences Institute, University of Toronto), Ilay Kamai (Physics Department, Technion Israel Institute of Technology)

EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves

Read the original on arXiv Machine Learning →

EncoTESS is a compact Time Series Foundation Model trained on TESS 2‑minute light curves that encodes stellar variability into a fixed‑size latent space, handling noise, irregular sampling, and data gaps. It improves age estimation for young K and M stars (≤100 Myr) and older M stars (≤1 Gyr) by outperforming traditional rotation period and variability amplitude indicators. The model’s lightweight architecture (~1 % of typical TSFMs) allows deployment on standard laptops and can be extended to other TESS cadences and missions like Kepler and PLATO.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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