msiudek/astroPT_euclid_VIS_model

AstroPT Euclid VIS Model

Pre-trained AstroPT model for single-band analysis using Euclid VIS imaging.

Overview

This is a pre-trained checkpoint for the AstroPT framework, trained on Euclid VIS band imaging from the Euclid Q1 dataset.

Citation: Euclid Collaboration: Siudek, M et al. 2025 (arXiv:2503.15312)

Quick Start

Load Model

import torch
from pathlib import Path

# Load model checkpoint
model_path = "astropt/090M/ckpt.pt"
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load state dict
checkpoint = torch.load(model_path, map_location=device)
# Initialize your model architecture here
# model.load_state_dict(checkpoint)

Inference

from datasets import load_dataset
import torch

# Load dataset
dataset = load_dataset(
    "msiudek/astroPT_euclid_dataset",
    split="train_batch_1",
    streaming=True
)

# Run inference
model.eval()
with torch.no_grad():
    for sample in dataset:
        vis_image = sample['VIS_image']  # 224×224
        vis_image = torch.tensor(vis_image, dtype=torch.float32)
        vis_image = vis_image.unsqueeze(0).unsqueeze(0)  # [1, 1, 224, 224]
        
        # Get embeddings
        embeddings = model(vis_image)

Training Data

• Dataset: AstroPT Euclid Dataset
• VIS Band: Euclid VIS (0.55–0.90 μm)

Related Models

• AstroPT VIS+NISP Model: Multi-band (VIS + NISP)
• AstroPT VIS+NISP+SED Model: Multi-modal (imaging + photometry)

Datasets

• Imaging: astroPT_euclid_dataset
• Metadata: astroPT_euclid_metadata

Code & Documentation

For inference code, training scripts, and tutorials, visit the AstroPT GitHub Repository.

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

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Last Updated: December 2025
Model Version: 1.0