# AI in Gravitational Wave Analysis, an Overview
## 4 Binary Black Hole Signal Detection  
Binary black holes are some of the most fascinating objects in the universe, and detecting their gravitational waves can provide crucial insights into the nature of black holes, their formation, and the evolution of galaxies. In recent years, the detection and analysis of gravitational waves from binary black holes have significantly enhanced with the application of deep learning techniques. The following papers have proposed various machine learning and deep learning models to enhance the detection and analysis of gravitational waves. Ref. [17] proposed a deep learning model for the real-time detection and parameter estimation of binary black hole mergers. The model is based on a convolutional neural network (CNN) and extends an algorithm formulated in their previous work [35], including an analysis of real LIGO data. Furthermore, they propose a deep neural network-based system that can enable real-time multi-messenger astrophysics by combining gravitational wave detection with other forms of astrophysical observations. The model uses classical feedforward networks and is proposed as an attempt to define a single pipeline that is capable of solving problems, like identifying the presence or absence of GW signals, classifying noise transients, and reconstructing the astrophysical properties of detected GW sources. A different model was exploited in ref. [36] in order to explore the use of deep networks to match the matched-filter model sensitivity, ref. [37], in face of lower latency in gravitational wave signals from binary black hole detection. ref. [36] used a CNN architecture and demonstrated that it could significantly reduce the computational cost of matched filtering. A more traditional way of using AI was conducted in ref. [38]; the authors defined a machine learning-based approach to rank the candidate signals detected by gravitational wave detectors. They used a gradient boosting machine and achieved help in identifying the most significant signals and reducing the false alarm rate. Similarly, ref. [39] proposed a machine learning-based classifier to identify inspiral signals in non-ideal single-detector data. The model is based on a random forest approach and can help improve the detection of gravitational waves from binary black holes using single detectors. A newer approach was proposed in ref. [40], which uses a convolutional transformer-based architecture to detect gravitational waves from binary black holes. The model architecture is built on a transformer, employs self-attention mechanisms, can significantly reduce computational costs, and improve detection accuracy with respect to purely convolutional-based architectures. An interesting deep learning-based approach to detect gravitational waves from binary black holes using multiple detectors was proposed in ref. [41]. They exploit the benefits of already-known squeeze-and-excitation networks (SENets) from ref. [42], in combination with a soft-thresholding shrinkage function in order to define a new classification model that can significantly improve the signal-to-noise ratio and reduce false alarms with slight computational costs compared to classical CNNs. The authors of [43] focused on the inference phase of gravitational wave detection models. They integrated a modified version of the WaveNet model [44], optimized for inference, with high-performance computing (HPC) to improve the speed of gravitational wave detection, allowing for automatic and real-time detection of gravitational wave sig nals. The proposed model is trained on a large dataset of simulated signals to improve its accuracy and reliability. The authors demonstrate the effectiveness of their approach by achieving higher detection rates with lower false positives compared to existing methods. Overall, this paper presents an innovative application of deep learning and HPC for advancing the field of gravitational wave astronomy. Differently, in ref.