Radio Morphology Eval

radio-morphology-eval

STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer — DeMarco et al. (2026) (arXiv:2603.29660, 2026)

What this evaluates

Probes the transfer learning capability of self-supervised vision models on radio astronomy morphology classification tasks across heterogeneous imaging pipelines, telescopes, and label granularities.

Datasets

• MiraBest — total 1563; splits: test (1563)
• LoTSS DR2 — total 8805; splits: test (8805)
• Radio Galaxy Zoo DR1 — total 98391; splits: test (98391)

Metrics

• accuracy (primary) — range: [0, 1]
  • Standard classification accuracy: the fraction of correctly predicted morphology labels out of the total number of evaluated samples.

Input / output format

Input: Single-channel radio astronomy cutout images, standardized via per-image ZScale contrast stretch, replicated to three channels, and resized to 224×224 (ViT-MAE) or 518×518 (DINOv2).

Output: Discrete morphology class label (e.g., FR I/FR II for MiraBest; FR I, FR II, Hybrid, Spiral, Relaxed double for LoTSS; 1c1p, 1c2p, 1c3p, 2c2p, 2c3p, 3c3p for RGZ).

Scoring recipe

def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return correct / len(gold_labels)

Common pitfalls

• Excluding uncertain labels or hybrid sources significantly alters dataset size and class distribution.
• Model input resolution is backbone-dependent (224 vs 518), which must be accounted for when comparing performance across architectures.
• FITS cutouts may contain NaN/Inf or blank values that require explicit filtering before evaluation.

Evidence (verbatim from paper)

We evaluate transfer learning on three public, morphology-focused radio astronomy imaging datasets that span different telescopes, labeling paradigms, and class granularities: MiraBest, the LoTSS DR2 visual-classification sample of Horton et al. (2025), and Radio Galaxy Zoo DR1 (Wong et al., 2024).

Citation

@misc{demarco2026stradavit,
  title={STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer},
  author={DeMarco et al. (2026)},
  year={2026},
  note={arXiv:2603.29660}
}

• arXiv: 2603.29660