File size: 3,062 Bytes
815eee5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | ---
license: bsd-3-clause
tags:
- atmospheric-retrieval
- exoplanets
- simulation-based-inference
pretty_name: SBI_INAF
size_categories:
- 10K<n<100K
---
# SBI_INAF
**SBI_INAF** is a dataset designed for **Simulation-Based Inference (SBI)** on atmospheric retrieval of exoplanets at a spectral resolution of approximately **R = 40000**, tailored for the GIANO-B echelle spectrograph. It contains transmission spectra (stellar and telluric free) together with their associated physical parameters, auxiliary metadata, normalization statistics, and predefined train/validation/test splits.
The dataset is built upon the Atmospheric Big Challenge (ABC) database, with major modifications to address the research goals of the research paper "Towards Scalable High-Resolution Atmospheric Retrieval of Exoplanets with Flow Matching" - M. Giordano Orsini et al., 2026.
The dataset is intended for supervised learning tasks involving parameter estimation from synthetic or simulated transmission spectra.
---
## Dataset Structure
```text
SBI_INAF_R1500/
├── AuxillaryTable.csv
├── FM_Parameter_Table.csv
├── h5s/
├── norm_params.pkl
├── QuartilesTable.csv
├── test_indices.npy
├── train_indices.npy
├── val_indices.npy
├── wavelengths.npy
├── widths.npy
```
---
## File Descriptions
| File | Description |
|------|-------------|
| `h5s/` | Collection of HDF5 files containing the spectra and associated sample information. |
| `FM_Parameter_Table.csv` | Physical parameters (forward-model parameters) associated with each simulated spectrum. These serve as prediction targets. |
| `AuxillaryTable.csv` | Additional metadata describing each sample (planetary system-related parameters). |
| `QuartilesTable.csv` | Summary statistics (quartiles) of selected parameters or features. Useful for exploratory data analysis. |
| `norm_params.pkl` | Stored normalization parameters used during preprocessing. |
| `wavelengths.npy` | Common wavelength grid for all spectra. |
| `widths.npy` | Spectral bin widths corresponding to the wavelength grid. |
| `train_indices.npy` | Indices defining the training split. |
| `val_indices.npy` | Indices defining the validation split. |
| `test_indices.npy` | Indices defining the held-out test split. |
---
## Citation
If this dataset contributes to published work, please cite:
```bibtex
@article{GiordanoOrsini_2026,
author = {Giordano Orsini, Massimiliano and Ferone, Alessio and Inno, Laura and Maratea, Antonio and Casolaro, Angelo and Giacobbe, Paolo and Pino, Lorenzo and Bonomo, Aldo S.},
title = {Towards Scalable High-Resolution Atmospheric Retrieval of Exoplanets with Flow Matching},
year = 2026,
journal = {Journal of Computational Physics},
addendum = {(Submitted)},
}
```
---
## License
Please refer to the accompanying repository or publication for licensing information.
---
## Contact
For questions regarding the dataset, please contact the dataset maintainers or the authors of the accompanying publication. |