Datasets:
File size: 6,794 Bytes
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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}
}
``` |