AstroM3Dataset

Description

AstroM3Dataset is a time-series astronomy dataset containing photometry, spectra, and metadata features for variable stars. The dataset was constructed by cross-matching publicly available astronomical datasets, primarily from the ASAS-SN (Shappee et al. 2014) variable star catalog (Jayasinghe et al. 2019) and LAMOST spectroscopic survey (Cui et al. 2012), along with data from WISE (Wright et al. 2010), GALEX (Morrissey et al. 2007), 2MASS (Skrutskie et al. 2006) and Gaia EDR3 (Gaia Collaboration et al. 2021).

The dataset includes multiple subsets (full, sub10, sub25, sub50) and supports different random seeds (42, 66, 0, 12, 123). Each sample consists of:

• Photometry: Light curve data of shape (N, 3) (time, flux, flux_error).
• Spectra: Spectra observations of shape (M, 3) (wavelength, flux, flux_error).
• Metadata:
  • meta_cols: Dictionary of metadata feature names and values.
  • photo_cols: Dictionary of photometric feature names and values.
• Label: The class name as a string.

Corresponding paper and code

• Paper: AstroM³: A self-supervised multimodal model for astronomy
• Code Repository: GitHub: AstroM³
• Processed Data: MeriDK/AstroM3Processed

Note: The processed dataset AstroM3Processed is created from the original dataset AstroM3Dataset by using preprocess.py

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Subsets and Seeds

AstroM3Dataset is available in different subset sizes:

• full: Entire dataset
• sub50: 50% subset
• sub25: 25% subset
• sub10: 10% subset

Each subset is sampled from the respective train, validation, and test splits of the full dataset.
For reproducibility, each subset is provided with different random seeds:

• 42, 66, 0, 12, 123

Data Organization

The dataset is organized as follows:

AstroM3Dataset/
├── photometry.zip             # Contains all photometry light curves
├── utils/
│   ├── parallelzipfile.py     # Zip file reader to open photometry.zip
├── spectra/                   # Spectra files organized by class
│   ├── EA/
│   │   ├── file1.dat
│   │   ├── file2.dat
│   │   ├── ...
│   ├── EW/
│   ├── SR/
│   ├── ...
├── splits/                    # Train/val/test splits for each subset and seed
│   ├── full/
│   │   ├── 42/
│   │   │   ├── train.csv
│   │   │   ├── val.csv
│   │   │   ├── test.csv
│   │   │   ├── info.json      # Contains feature descriptions and preprocessing info
│   │   ├── 66/
│   │   ├── 0/
│   │   ├── 12/
│   │   ├── 123/
│   ├── sub10/
│   ├── sub25/
│   ├── sub50/
│── AstroM3Dataset.py          # Hugging Face dataset script

Usage

To load the dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Load the default full dataset with seed 42
dataset = load_dataset("MeriDK/AstroM3Dataset", trust_remote_code=True)

The default configuration is full_42 (entire dataset with seed 42). To load a specific subset and seed, use {subset}_{seed} as the name:

from datasets import load_dataset

# Load the 25% subset sampled using seed 123
dataset = load_dataset("MeriDK/AstroM3Dataset", name="sub25_123", trust_remote_code=True)

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Citation

🤗 If you find this dataset usefull, please cite our paper 🤗

@article{rizhko2024astrom,
  title={AstroM $\^{} 3$: A self-supervised multimodal model for astronomy},
  author={Rizhko, Mariia and Bloom, Joshua S},
  journal={arXiv preprint arXiv:2411.08842},
  year={2024}
}

References

1. Shappee, B. J., Prieto, J. L., Grupe, D., et al. 2014, ApJ, 788, 48, doi: 10.1088/0004-637X/788/1/48
2. Jayasinghe, T., Stanek, K. Z., Kochanek, C. S., et al. 2019, MNRAS, 486, 1907, doi: 10.1093/mnras/stz844
3. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197, doi: 10.1088/1674-4527/12/9/003
4. Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868
5. Morrissey, P., Conrow, T., Barlow, T. A., et al. 2007, ApJS, 173, 682, doi: 10.1086/520512
6. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708
7. Gaia Collaboration, Brown, A. G. A., et al. 2021, AAP, 649, A1, doi: 10.1051/0004-6361/202039657