--- license: mit dataset_info: features: - name: sourceid dtype: string - name: bands_data struct: - name: g struct: - name: feat_dynamic_real list: float64 - name: length dtype: int64 - name: mjd list: float64 - name: past_feat_dynamic_real list: float64 - name: target list: float64 - name: i struct: - name: feat_dynamic_real list: float64 - name: length dtype: int64 - name: mjd list: float64 - name: past_feat_dynamic_real list: float64 - name: target list: float64 - name: r struct: - name: feat_dynamic_real list: float64 - name: length dtype: int64 - name: mjd list: float64 - name: past_feat_dynamic_real list: float64 - name: target list: float64 - name: period dtype: float64 - name: class_str dtype: string - name: ra dtype: float64 - name: dec dtype: float64 splits: - name: train num_bytes: 874152765 num_examples: 29047 - name: validation num_bytes: 125721375 num_examples: 4150 - name: test num_bytes: 247388574 num_examples: 8301 - name: anom num_bytes: 32708516 num_examples: 1087 download_size: 557951471 dataset_size: 1279971230 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* - split: anom path: data/anom-* --- # StarEmbed Benchmark Data set The StarEmbed benchmark data set is available here in huggingface datasets format. The ~40,000 multi-band ZTF light curves are available at `StarEmbed/data`. The train/validation/test splits are included alongside the additional anom split used for the OOD benchmarking. ## Available columns Each star in data set has the following fields: **Top-level columns** | Column | Type | Description | | ------------ | ---------------------------------- | -------------------------------------------------------------------------------------------------------- | | `sourceid` | string | Catalina Surveys DR1 (CSDR1) source id of the variable star (e.g. `CSS_J082956.4-044426`) | | `bands_data` | dict / struct (keys `g`, `i`, `r`) | per-band light curves; each band is a struct (or `null` if absent) holding the arrays in the table below.| | `period` | float64 | catalog period of the variable star, in days (range ≈ 0.13 – 885) | | `class_str` | string | variable-star class label; one of `EW`, `EA`, `RRab`, `RRc`, `RRd`, `RS CVn`, `LPV` | | `ra` | float64 | right ascension (J2000) in decimal degrees | | `dec` | float64 | declination (J2000) in decimal degrees | **Per-band fields inside each band of `bands_data` (`g` / `r` / `i`)** | Field | Type | Description | | ------------------------ | -------------- | ------------------------------------------------------------------- | | `target` | list of floats | magnitude, measure of brightness (AB system) — the raw light curve | | `past_feat_dynamic_real` | list of floats | mag_err, 1σ uncertainty on magnitude, aligned with `target` | | `feat_dynamic_real` | list of floats | delta_t, time gap (days) between each two consecutive observations | | `mjd` | list of floats | observation time in Modified Julian Date, aligned with `target` | | `length` | int64 | number of observations in this band's light curve | All four splits (train, validation, test, anom) share the identical schema described above. The train/validation/test splits contain the seven in-distribution classes listed under `class_str`, whereas the anom split is an anomaly-detection holdout whose `class_str` values are a disjoint set of out-of-distribution classes — `Beta_Lyrae`, `Blazhko`, `ACEP`, `Cep-II`, `HADS`, `LADS`, `ELL`, `Hump`, `PCEB`, `EA_UP` — that never appear in training. --- ## **Citation** This work was accepted to the main track of ICML 2026 as well as the AI4Physics workshop. If you use or refer to this dataset please cite our work. ``` @article{StarEmbed, author = {{Li}, Weijian and {Chen}, Hong-Yu and {Rehemtulla}, Nabeel and {Shah}, Ved G. and {Wu}, Dennis and {Kim}, Dongho and {Lin}, Qinjie and {Miller}, Adam A. and {Liu}, Han}, title = "{StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars}", journal = {arXiv e-prints}, keywords = {Solar and Stellar Astrophysics, Instrumentation and Methods for Astrophysics, Artificial Intelligence}, year = 2025, month = oct, eid = {arXiv:2510.06200}, pages = {arXiv:2510.06200}, doi = {10.48550/arXiv.2510.06200}, archivePrefix = {arXiv}, eprint = {2510.06200}, primaryClass = {astro-ph.SR}, adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv251006200L}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } ``` The StarEmbed data set is curated from ZTF observations and the Catalina Surveys Periodic Variable Star Catalog. If you use the StarEmbed data please also cite the works below. ``` @article{bellm2018zwicky, title={The Zwicky Transient Facility: system overview, performance, and first results}, author={Bellm, Eric C and Kulkarni, Shrinivas R and Graham, Matthew J and Dekany, Richard and Smith, Roger M and Riddle, Reed and Masci, Frank J and Helou, George and Prince, Thomas A and Adams, Scott M and others}, journal={Publications of the Astronomical Society of the Pacific}, volume={131}, number={995}, pages={018002}, year={2018}, publisher={IOP Publishing} } @article{drake2014catalina, title={The catalina surveys periodic variable star catalog}, author={Drake, AJ and Graham, MJ and Djorgovski, SG and Catelan, M and Mahabal, AA and Torrealba, G and Garc{\'\i}a-{\'A}lvarez, D and Donalek, C and Prieto, JL and Williams, R and others}, journal={The Astrophysical Journal Supplement Series}, volume={213}, number={1}, pages={9}, year={2014}, publisher={IOP Publishing} }025} } ```