# AI in Gravitational Wave Analysis, an Overview
## 5 Gravitational Wave Bursts  
Gravitational wave bursts encompass a diverse array of transient events that can be categorized as unmodeled signals, exhibiting sudden and intense releases of gravitational wave energies over short time periods [47]. These bursts--searched for with unmodeled searchers--may originate from a variety of astrophysical phenomena, including core-collapse supernovae, cosmic string cusps, and other cataclysmic events that defy precise parameterization [48; 49]. The exploration of unmodeled burst searches presents unique challenges and requires sophisticated data analysis techniques that are capable of identifying and characterizing these transient, impulsive signals [50]. This distinction between modeled and unmodeled searches enriches the understanding of gravitational wave detection strategies and underscores the necessity of tailored approaches for each category.  
To analyze gravitational wave bursts, scientists employ sophisticated data analysis techniques, including time-frequency analysis methods, machine learning, and deep learning algorithms. These approaches help identify and characterize bursts amidst background noise and extract valuable information about their origin, energy, and other relevant parameters. In more detail, a novel approach using artificial neural networks (ANNs) is presented in ref. [51] to identify GW signals produced with short gamma-ray bursts (SGRBs). The authors demonstrate that ANNs can improve the sensitivity of detecting these GW signals by reducing noise in the data. In more detail, they used a multi-layer perceptron (MLP) model, which shows superior performance to traditional statistical detection methods when applied to data from advanced LIGO and Virgo detectors.  
Different works have exploited the use of CNNs and compared their results with other techniques, such as in ref. [18; 52]. They both focused on using CNNs to detect and classify gravitational wave signals from core-collapse supernovae. The first work compared two convolutional neural networks (CNNs), one-dimensional and two-dimensional architectures, in order to analyze data from the advanced LIGO and Virgo detectors; the authors demonstrated that their approach can achieve high levels of accuracy (\(\geq\)95%) in detecting and classifying signals. Similarly, the latter used a CNN-based architecture; the authors demonstrated the effectiveness of their approach by comparing the performance of their CNN model with traditional matched-filtering techniques. In both cases, the results show that deep learning approaches achieve higher detection rates with lower false alarms compared to traditional methods. A more in-depth analysis was performed in ref. [53]; the authors investigated the use of deep learning algorithms for detecting gravitational wave signals from core-collapse supernovae. The authors used three different reduced CNN architectures from the SoA models: ResNet, Inception v4, and Inception-ResNet v1. The models were reduced by minimizing the number of parameters, using, for example,minimal amounts of pooling layers and skip connections, in order to achieve better efficiency and inference times, demonstrating the feasibility of designing new, low-latency detection pipelines for CCSN.