# AI in Gravitational Wave Analysis, an Overview
## 1 Introduction  
Gravitational waves have presented researchers with a multitude of intriguing challenges, each of which has led to significant advancements in the field. In particular, several key research problems have emerged, including glitch classification, glitch cancellation, gravitational wave denoising, binary black hole signal detection, gravitational wave bursts, and various minor problems that contribute to the overall understanding of gravitational wave phenomena. Within the domain of gravitational wave astronomy, a \"glitch\" denotes an abrupt and transient perturbation observed in the data emanating from pulsars or neutron stars. Pulsars, characterized as rapidly rotating neutron stars emitting regular electromagnetic pulses, are indispensable tools for investigating various astrophysical phenomena, including gravitational waves [1]. However, the unparalleled stability of pulsar signals can occasionally be disrupted by these so-called \"glitches\". Glitches are sudden enhancements in a pulsar's rotation rate, resulting in a temporary alteration of its observed frequency, otherwise referred to as the spin frequency [2]. These anomalies stem from intricate processes within the pulsar, such as the interaction between its superfluid interior and solid crust, leading to an exchange of angular momentum [3]. The consequences of glitches extend deeply into the domain of gravitational wave analysis, particularly concerning continuous gravitational wave sources. These sources, driven by persistent emission of gravitational waves, frequently arise from non-axisymmetric deformations or instabilities in neutron stars [4]. Monitoring pulsar rotations and detecting glitches furnish researchers with invaluable insights into the internal dynamics and fundamental characteristics of neutron stars [5]. However, the presence of glitches introducescomplexities into gravitational wave searches. The abrupt alteration in a pulsar's spin frequency momentarily affects the precision of predictions for its timing behavior, potentially confounding the distinction between gravitational wave signals and glitches during data analysis [6]. Glitch classification is a fundamental and essential task in gravitational wave research. It revolves around the identification and categorization of unexpected noise or anomalies present in the data collected by gravitational wave detectors [7]. Precise classification of glitches holds immense importance as it enables the differentiation of genuine gravitational wave signals from various noise sources. Early research efforts in glitch classification focused on techniques, such as principal component analysis (PCA) [8; 9] and multi-layer perceptron (MLP) [10], which demonstrated some success in automating the classification process. Building upon these foundations, subsequent studies explored the application of Gaussian clustering, Bayesian models, and wavelet detection filters, pushing the boundaries of glitch classification even further. Nonetheless, recent advancements in deep learning have showcased the remarkable potential of convolutional neural networks (CNNs) [11] for glitch classification [12]. By employing the time-frequency image analysis, CNNs have proven to be effective tools for automating and improving glitch classification accuracy. Other classes of numerical models for glitch analysis attempt to disentangle glitches into their speculated elementary constituents, ref. [13]. Other pressing challenges in gravitational wave studies are glitch cancellation and denoising of gravitational wave signals [14]. The primary objective here is to differentiate genuine signals from unwanted noise and eliminate identified glitches, thereby achieving high-precision measurements of gravitational waves. Noise in gravitational wave signals can originate from a variety of sources, including seismic activity, thermal fluctuations, and electronic noise.