Datasets:

msiudek
/

astroPT_euclid_VIS_embeddings

dataset_info:
  features:
    - name: object_id
      dtype: int64
    - name: embedding
      sequence: float32
  splits:
    - name: train
      num_bytes: 818515188
      num_examples: 265407
    - name: test
      num_bytes: 204632652
      num_examples: 66353
  download_size: 1230299259
  dataset_size: 1023147840
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

AstroPT Euclid Embeddings

Pre-computed embeddings from the AstroPT VIS Model on Euclid dataset samples.

Overview

This repository contains pre-computed feature embeddings generated by AstroPT models applied to the Euclid Q1 galaxy dataset. These embeddings can be used for

Efficient downstream task training (reduced computational cost)
Feature analysis and visualization
Similarity search and retrieval
Clustering and unsupervised learning
Fast fine-tuning on specialized tasks

Example notebooks are found in the AstroPT scripts

Available embeddings:

VIS: Single-band embeddings from Euclid VIS imaging; VIS embeddings
VIS+NISP: Multi-band embeddings from VIS + 3× NIR (Y, J, H); VIS+NISP embeddings
VIS+NISP+SED: Multi-modal embeddings from imaging + 13-band photometry; VIS+NISP+SED embeddings

Quick Start

Load VIS Embeddings

from datasets import load_dataset

# Load VIS embeddings
embeddings = load_dataset(
    "msiudek/astroPT_euclid_VIS_embeddings",
    split="train",
    streaming=True
)

# View a sample
sample = embeddings[0]
print(f"Object ID: {sample['object_id']}")
print(f"Embedding shape: {len(sample['embedding'])}")
print(f"Embedding: {sample['embedding']}")

Related Resources

Models:

Datasets:

AstroPT Euclid Dataset: Original imaging + photometry
AstroPT Euclid Metadata: Galaxy properties

Code:

AstroPT GitHub: Training and inference code

Citation

@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}
}

License

CC-BY-4.0

Last Updated: December 2025
Embeddings Version: 1.0