  Recent advancements in gravitational wave astronomy have seen the application
of convolutional neural networks (CNNs) in signal detection from compact binary
coalescences. This study presents a comparative analysis of two CNN
architectures: one-dimensional (1D) and two-dimensional (2D) along with an
ensemble model combining both. We trained these models to detect gravitational
wave signals from binary black hole (BBH) mergers, neutron star-black hole
(NSBH) mergers, and binary neutron star (BNS) mergers within real detector
noise. Our investigation entailed a comprehensive evaluation of the detection
performance of each model type across different signal classes. To understand
the models' decision-making processes, we employed feature map visualization
and attribution analysis. The findings revealed that while the 1D model showed
superior performance in detecting BBH signals, the 2D model excelled in
identifying NSBH and BNS signals. Notably, the ensemble model outperformed both
individual models across all signal types, demonstrating enhanced detection
capabilities. Additionally, input feature visualization indicated distinct
areas of focus in the data for the 1D and 2D models, emphasizing the
effectiveness of their combination.
