Unsupervised Morphological Characterization of Gravitational-Wave Glitches in LIGO O4a Using Frozen DINOv2 Features

A central open question in gravitational-wave detector characterization is whether the O4a observing run has introduced glitch morphologies not present in earlier runs. We present gravi-signal-ml, an open-source pipeline for unsupervised morphological characterization of instrumental noise transients (glitches) in LIGO gravitational-wave data, applied to 1,277 hours of public O4a strain data from the Hanford and Livingston detectors. The pipeline extracts 384-dimensional visual embeddings from Q-transform spectrograms using a frozen DINOv2 Vision Transformer with register tokens (ViTS/14), requiring no labeled training data. Embeddings are projected via PCA and UMAP with cosine metric, then clustered using a Dirichlet Process Mixture Model (DPMM). Cluster robustness is systematically assessed through ablation studies, stability analysis across hyperparameter perturbations, and morphological cross-check against an in-domain Gravity Spy O3b reference index. A time-slide background test excludes statistically significant H1--L1 coincidences ($p \geq 0.1$) in all sessions. Across 188,000+ spectrograms, no morphologically novel glitch candidates were identified -- all anomalous clusters map to known Gravity Spy classes with cosine similarity $> 0.98$. L1 embeddings show consistently high robustness (ablation ARI $> 0.90$ in all four sessions), while H1 exhibits lower and more variable grayscale ablation ARI ($\sim 0.68$--$0.90$), suggesting a structural difference in the H1 noise manifold under DINOv2 feature extraction. This null result, obtained with a fully validated pipeline, establishes a reproducible baseline for zero-shot glitch morphology characterization in O4a data. The pipeline and all results are publicly available at https://github.com/lucacirfeta/dante-gravi-signal-ml DOI: https://doi.org/10.5281/zenodo.20121860.