Gravitational Wave Echo Detection via Deep Temporal Convolutional Networks and Anomaly Scoring

**Abstract:** This research investigates a novel methodology for identifying gravitational wave (GW) echoes—transient signals predicted by some modified theories of gravity—within LIGO-Virgo data. We propose a Deep Temporal Convolutional Network (DTCN) architecture, coupled with a rigorous anomaly scoring system based on Gaussian process regression, to distinguish echo signals from noise and astrophysical artifacts. The system leverages readily available, validated signal processing techniques and demonstrates significant potential for automated, real-time GW echo detection, paving the way for constraints on fundamental physics and modified gravity theories.