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Recent advancements in gravitational wave astronomy have seen the applicationof convolutional neural networks (CNNs) in signal detection from compact binarycoalescences. This study presents a comparative analysis of two CNNarchitectures: one-dimensional (1D) and two-dimensional (2D) along with anensemble model combining both. We trained these models to detect gravitationalwave signals from binary black hole (BBH) mergers, neutron star-black hole(NSBH) mergers, and binary neutron star (BNS) mergers within real detectornoise. Our investigation entailed a comprehensive evaluation of the detectionperformance of each model type across different signal classes. To understandthe models' decision-making processes, we employed feature map visualizationand attribution analysis. The findings revealed that while the 1D model showedsuperior performance in detecting BBH signals, the 2D model excelled inidentifying NSBH and BNS signals. Notably, the ensemble model outperformed bothindividual models across all signal types, demonstrating enhanced detectioncapabilities. Additionally, input feature visualization indicated distinctareas of focus in the data for the 1D and 2D models, emphasizing theeffectiveness of their combination.

Comparative Study of 1D and 2D CNN Models with Attribution Analysis for Gravitational Wave Detection from Compact Binary Coalescences