| --- |
| license: mit |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - astronomy |
| - multimodal |
| - classification |
| arxiv: |
| - arXiv:2411.08842 |
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| path: sub10_123/test-* |
| - config_name: sub10_42 |
| data_files: |
| - split: train |
| path: sub10_42/train-* |
| - split: validation |
| path: sub10_42/validation-* |
| - split: test |
| path: sub10_42/test-* |
| - config_name: sub10_66 |
| data_files: |
| - split: train |
| path: sub10_66/train-* |
| - split: validation |
| path: sub10_66/validation-* |
| - split: test |
| path: sub10_66/test-* |
| - config_name: sub25_0 |
| data_files: |
| - split: train |
| path: sub25_0/train-* |
| - split: validation |
| path: sub25_0/validation-* |
| - split: test |
| path: sub25_0/test-* |
| - config_name: sub25_12 |
| data_files: |
| - split: train |
| path: sub25_12/train-* |
| - split: validation |
| path: sub25_12/validation-* |
| - split: test |
| path: sub25_12/test-* |
| - config_name: sub25_123 |
| data_files: |
| - split: train |
| path: sub25_123/train-* |
| - split: validation |
| path: sub25_123/validation-* |
| - split: test |
| path: sub25_123/test-* |
| - config_name: sub25_42 |
| data_files: |
| - split: train |
| path: sub25_42/train-* |
| - split: validation |
| path: sub25_42/validation-* |
| - split: test |
| path: sub25_42/test-* |
| - config_name: sub25_66 |
| data_files: |
| - split: train |
| path: sub25_66/train-* |
| - split: validation |
| path: sub25_66/validation-* |
| - split: test |
| path: sub25_66/test-* |
| - config_name: sub50_0 |
| data_files: |
| - split: train |
| path: sub50_0/train-* |
| - split: validation |
| path: sub50_0/validation-* |
| - split: test |
| path: sub50_0/test-* |
| - config_name: sub50_12 |
| data_files: |
| - split: train |
| path: sub50_12/train-* |
| - split: validation |
| path: sub50_12/validation-* |
| - split: test |
| path: sub50_12/test-* |
| - config_name: sub50_123 |
| data_files: |
| - split: train |
| path: sub50_123/train-* |
| - split: validation |
| path: sub50_123/validation-* |
| - split: test |
| path: sub50_123/test-* |
| - config_name: sub50_42 |
| data_files: |
| - split: train |
| path: sub50_42/train-* |
| - split: validation |
| path: sub50_42/validation-* |
| - split: test |
| path: sub50_42/test-* |
| - config_name: sub50_66 |
| data_files: |
| - split: train |
| path: sub50_66/train-* |
| - split: validation |
| path: sub50_66/validation-* |
| - split: test |
| path: sub50_66/test-* |
| --- |
| # AstroM3Processed |
|
|
| ## Description |
|
|
| AstroM3Processed 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, 9)` (time, flux, flux\_error, amplitude, period, lksl_statistic, rfr_score, mad, delta_t). |
| - **Spectra**: Spectra observations of shape `(3, 2575)` (wavelength, flux, flux\_error). |
| - **Metadata**: List of metadata values of shape `(34,)` |
| - **Label**: The class name as int. |
| |
| ## Corresponding paper and code |
| |
| - Paper: [AstroM<sup>3</sup>: A self-supervised multimodal model for astronomy](https://arxiv.org/abs/2411.08842) |
| - Code Repository: [GitHub: AstroM<sup>3</sup>](https://github.com/MeriDK/AstroM3/) |
| - Original Data: [AstroMLCore/AstroM3Dataset](https://huggingface.co/datasets/AstroMLCore/AstroM3Dataset/) |
| |
| **Note:** The processed dataset `AstroM3Processed` is created from the original dataset `AstroM3Dataset` |
| by using [preprocess.py](https://huggingface.co/datasets/AstroMLCore/AstroM3Dataset/blob/main/preprocess.py) |
| |
| ## 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` |
| |
| ## Usage |
| To load the dataset using the Hugging Face `datasets` library, specify the name in the format "{subset}_{seed}". For example: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the full dataset with seed 42 |
| dataset = load_dataset("AstroMLCore/AstroM3Processed", name="full_42") |
| |
| # Load the 25% subset sampled using seed 123 |
| dataset = load_dataset("AstroMLCore/AstroM3Processed", name="sub25_123") |
| ``` |
|
|
| --- |
|
|
| ## Citation |
| 🤗 If you find this dataset usefull, please cite our paper 🤗 |
| ```bibtex |
| @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 |
|
|
