arXiv AI By Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla, Ved G. Shah, Dongho Kim, Dennis Wu, Qinjie Lin, Adam A. Miller, Han Liu

StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars

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arXiv:2510. 06200v4 Announce Type: replace-cross Abstract: Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling.

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arXiv Machine Learning
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Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

arXiv:2607. 00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy.

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EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves

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.

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)
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Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

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