README.md

---
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---
# AstroPT Euclid Dataset

A large-scale multi-band astronomical imaging and photometric dataset from the Euclid Q1 dataset, designed for training deep learning models for astronomical analysis.

## Overview

This dataset contains **331,759** Euclid bright and large galaxies (HE<22.5 & segmentation_area>0) with simultaneous observations in visible and near-infrared bands from the Euclid mission, complemented by optical photometry from ground-based surveys. It is specifically curated for the ([AstroPT](https://github.com/Smith42/astroPT)) framework for multi-modal learning tasks in astronomy.

**Citation**: Euclid Collaboration: Siudek, M et al. 2025 ([arXiv:2503.15312](https://ui.adsabs.harvard.edu/abs/2025arXiv250315312E/abstract))

## Data Schema

## Images
- **`VIS_image`** (224×224): Visible-band imaging from Euclid VIS instrument (0.55–0.90 μm)
- **`NISP_Y_image`** (224×224): Near-infrared Y-band from Euclid NISP (1.0–1.2 μm)
- **`NISP_J_image`** (224×224): Near-infrared J-band from Euclid NISP (1.2–1.4 μm)
- **`NISP_H_image`** (224×224): Near-infrared H-band from Euclid NISP (1.4–2.0 μm)
- **`RGB_image`**: RGB composite image

### Photometry
- **`SED`**: Spectral Energy Distribution from optical/NIR photometric filters:
  1. u-band (CFHT MegaCam)
  2. g-band (Subaru HSC)
  3. r-band (CFHT MegaCam)
  4. i-band (Pan-STARRS)
  5. z-band (Subaru HSC)
  6. g-band (DECam)
  7. r-band (DECam)
  8. i-band (DECam)
  9. z-band (DECam)
  10. VIS-band (Euclid)
  11. Y-band (Euclid NISP)
  12. J-band (Euclid NISP)
  13. H-band (Euclid NISP)

### Metadata
- **`object_id`** (int64): Unique astronomical object identifier

## Quick Start

### Load Dataset (Streaming Mode - Recommended)

```python
from datasets import load_dataset

# Load a single training batch without caching
dataset = load_dataset(
    "msiudek/astroPT_euclid_dataset",
    split="train_batch_1",
    streaming=True
)

# Iterate through samples
for sample in dataset:
    object_id = sample['object_id']
    vis_image = sample['VIS_image']      # 224×224 array
    nisp_y = sample['NISP_Y_image']      # 224×224 array
    nisp_j = sample['NISP_J_image']      # 224×224 array
    nisp_h = sample['NISP_H_image']      # 224×224 array
    sed = sample['SED']                  # 13 photometric fluxes
    
    # Your processing logic here
```

### Load Multiple Batches

```python
from datasets import load_dataset
from huggingface_hub import list_repo_files

# Get all available splits
files = list_repo_files("msiudek/astroPT_euclid_dataset", repo_type="dataset")
parquets = [f for f in files if '.parquet' in f]
batches = set(f.split('/')[-1].rsplit('-', 2)[0] for f in parquets)

# Process all training batches
train_batches = sorted([b for b in batches if 'train_batch' in b])

for batch_name in train_batches:
    try:
        dataset = load_dataset(
            "msiudek/astroPT_euclid_dataset",
            split=batch_name,
            streaming=True
        )
        for sample in dataset:
            # Process sample
            pass
    except Exception as e:
        print(f"Error loading {batch_name}: {e}")
```

### Load Non-Streaming (Download to Disk)

```python
dataset = load_dataset(
    "msiudek/astroPT_euclid_dataset",
    streaming=False,
    cache_dir="/path/to/cache"
)
```

## Data Access & Citation

**Framework**: [AstroPT GitHub Repository](https://github.com/Smith42/astroPT)
- Multi-modal learning framework
- Pre-training and fine-tuning code
- Example notebooks and tutorials
  
**Metadata**: [astroPT_euclid_metadata](https://huggingface.co/datasets/msiudek/astroPT_euclid_metadata)
- Morphological properties from Euclid
- Physical properties (stellar mass, SFR, etc.)
- Spectroscopic redshifts from DESI (~5% of sample)
- 
**Citation**: 
```bibtex
@article{Siudek2025,
  title={AstroPT: Astronomical Physics Transformers for Multi-modal Learning},
  author={Siudek, M and others},
  journal={Euclid Collaboration},
  eprint={2503.15312},
  archivePrefix={arXiv},
  year={2025},
  url={https://ui.adsabs.harvard.edu/abs/2025arXiv250315312E/abstract}
}
```
**Last Updated**: December 2025  
**Dataset Version**: 1.0  
**Euclid Release**: Q1
**Data License**: CC-BY-4.0

## Acknowledgments

This dataset was generated using data from the **Euclid mission**, an ESA medium-class astronomy and astrophysics space mission. Euclid is a space mission of the European Space Agency (ESA).