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---
license: mit
pretty_name: AstroM3Dataset
size_categories:
- 10K<n<100K
tags:
- astronomy
- multimodal
- classification
arxiv:
- arXiv:2411.08842
---
# 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: [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/)
- Processed Data: [AstroMLCore/AstroM3Processed](https://huggingface.co/datasets/AstroMLCore/AstroM3Processed/)
**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`
## 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:
```python
from datasets import load_dataset
# Load the default full dataset with seed 42
dataset = load_dataset("AstroMLCore/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:
```python
from datasets import load_dataset
# Load the 25% subset sampled using seed 123
dataset = load_dataset("AstroMLCore/AstroM3Dataset", name="sub25_123", trust_remote_code=True)
```
---
## 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 |