{"ID":2859514,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06200","arxiv_id":"2510.06200","title":"StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars","abstract":"Time series foundation models (TSFMs) are increasingly being adopted as highly-capable general-purpose time series representation learners. Although their training corpora are vast, they exclude astronomical time series data. Observations of stars produce peta-scale time series with unique challenges including irregular sampling and heteroskedasticity. We introduce StarEmbed, the first public benchmark for rigorous and standardized evaluation of state-of-the-art TSFMs on stellar time series observations (``light curves''). We benchmark on three scientifically-motivated downstream tasks: unsupervised clustering, supervised classification, and out-of-distribution source detection. StarEmbed integrates a catalog of expert-vetted labels with multi-variate light curves from the Zwicky Transient Facility, yielding ~40k hand-labeled light curves spread across seven astrophysical classes. We evaluate the zero-shot representation capabilities of three TSFMs (MOIRAI, Chronos, Chronos-Bolt) and a domain-specific transformer (Astromer) against handcrafted feature extraction, the long-standing baseline in the astrophysics literature. Our results demonstrate that these TSFMs, especially the Chronos models, which are trained on data completely unlike the astronomical observations, can outperform established astrophysics-specific baselines in some tasks and effectively generalize to entirely new data. In particular, TSFMs deliver state-of-the-art performance on our out-of-distribution source detection benchmark. With the first benchmark of TSFMs on astronomical time series data, we test the limits of their generalization and motivate a paradigm shift in time-domain astronomy from using task-specific, fully supervised pipelines toward adopting generic foundation model representations for the analysis of peta-scale datasets from forthcoming observatories.","short_abstract":"Time series foundation models (TSFMs) are increasingly being adopted as highly-capable general-purpose time series representation learners. Although their training corpora are vast, they exclude astronomical time series data. Observations of stars produce peta-scale time series with unique challenges including irregula...","url_abs":"https://arxiv.org/abs/2510.06200","url_pdf":"https://arxiv.org/pdf/2510.06200v3","authors":"[\"Weijian Li\",\"Hong-Yu Chen\",\"Nabeel Rehemtulla\",\"Ved G. Shah\",\"Dennis Wu\",\"Dongho Kim\",\"Qinjie Lin\",\"Adam A. Miller\",\"Han Liu\"]","published":"2025-10-07T17:53:56Z","proceeding":"astro-ph.SR","tasks":"[\"astro-ph.SR\",\"astro-ph.IM\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}