★ GWAK2: Gravitational Wave Anomalous Knowledge using SSL ★

About

Since the first gravitational-wave detection by ground-based interferometers, after more than a decade of observations has yielded over one hundred compact binary coalescence (CBC) events, whose waveforms can be well-modeled by general relativity. These well-modeled signals enable detection pipelines based on matched filtering, which search for waveform consistency against the CBC template bank. However, within the sensitivity band of the LIGO–Virgo–KAGRA Collaboration detectors, a broader class of astrophysical sources such as core-collapse supernovae and other transient phenomena are poorly modeled or inherently unpredictable. Consequently, these sources cannot be efficiently captured by template-based searches, motivating the need for waveform-agnostic detection strategies. To address this challenge, we develop Gravitational Wave Anomalous Knowledge (GWAK), a machine learning–based search framework designed for generic transient detection. GWAK employs a semi-supervised embedding model using Self Supervised Learning to learn a low-dimensional representation of detector data, followed by a metric model trained on noise data to define a discriminative search space. This approach enables waveform-agnostic searches for gravitational-wave transients and improves the identification and characterization of unexpected signals.

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