# AI in Gravitational Wave Analysis, an Overview
## 2 Glitch Classification  
Glitch classification on gravitational waves is the process of identifying and categorizing unexpected noise or anomalies in the data collected by gravitational wave detectors. Glitches can arise from various sources, such as instrumental artifacts, environmental factors, or even cosmic events. Classifying these glitches is crucial in order to distinguish them from actual gravitational wave signals, which can provide valuable insights into the nature of the universe. The process of glitch classification involves analyzing the time-frequency patterns of the signal and comparing them to a database of known glitches.  
Machine learning algorithms are often used to automate this process and to improve the accuracy and efficiency of glitch classification. Through glitch classification, scientists hope to enhance the sensitivity and reliability of gravitational wave detectors and unlock new discoveries in the field of astrophysics. Before the breakthrough of AI models, gravitational waves and glitches were mainly analyzed with analytical models, as presented in [22], using a method based on triangular norms. Then, machine learning (ML) and deep learning (DL) models have been used to classify the glitch that occurred on a gravitational waveform to better address the method for its mitigation or deletion. Some of the first attempts in glitch classification research were conducted in [10; 23]. Both research groups developed signal artifact classification models based on machine learning. [PERSON]. [10] defined an approach based on principal component analysis (PCA), multi-layer perceptron (MLP), and self-organizing maps (SOMs); this approach detected and classified glitches in gravitational waveforms into two categories: bursts and glitches. Ref. [23] tested support vector machines and random forest on the same task. The second phase of research development in the glitch classification area is mainly conditioned by the application of models with deeper statistical underpinnings, such as Gaussian clustering and Bayesian models. Ref. [9] firstly performed a formulation of these models for glitch classification with simulated data and then [24] applied such models to real data. They represents works in this area with three different approaches: PCA with Gaussian mixture model (GMM); Bayesian models; wavelet detection filters with machine learning-based classification.  
Since 2017, a new class of approaches has taken hold, involving the time-frequency image analysis of glitch events, opening up the area of deep learning for gravitational wave analysis. This perspective enabled the implementation of convolutional neural networks (CNNs), which are particularly suitable for image classification and image analysis tasks. Ref. [20] presents one of the earliest applications of a CNN model for glitch classification on LIGO detector data, cited in ref. [25]. Then several improvements in classification performances have been achieved with CNN multi-view and parallel view, as introduced in ref. [26]. The multi-view technique consists of data pre-processing to merge images of glitches of different durations into a single image, combining the characteristics of different glitches. The parallel view technique is a more sophisticated algorithm that uses a group of parallel CNNs to extract relevant features from different images; these features are used to feed the last CNN that combines features from previous networks.  
CNNs have been compared with the classical machine learning approach, such as the support vector machine, as cited in ref. [12], with CNNs demonstrating better cross-class classification performance. Within the glitch classification area, ref. [27] proposed a method to predict whether a glitch is likely to appear at a given time based on gravitational wave detector auxiliary channels; see ref. [1]. This method is based on elastic-net-based machine learning for understanding (EMU).