Representation-Aware Self Supervised Learning for Gravitational Wave Science: Introducing GWCLIP

About

The next generation of gravitational wave detectors will produce data at a scale and complexity that renders traditional matched-filtering and parameter estimation pipelines insufficient as standalone tools. We argue that the field must shift toward representation-aware learning — building models that acquire rich, physics-informed embeddings of gravitational wave signals rather than optimising narrowly for a single downstream task. Inspired by contrastive multimodal frameworks such as CLIP, we introduce GWCLIP: a model trained to align gravitational wave strain data with their corresponding physical descriptions — waveform families, source parameters, and detector context — through contrastive objectives across a large and diverse signal corpus.

This shared embedding space enables zero/few-shot classification, rapid similarity search across catalogs, and robust transfer to downstream tasks including glitch rejection, denoising and parameter inference with minimal labeled data. We demonstrate that GWCLIP embeddings capture physically meaningful structure, clustering signals by source parameters in a geometry that generalises across detector configurations.

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