Evaluating Deep Learning Models for Multiclass Classification of LIGO Gravitational-Wave Glitches
Gravitational-wave detectors are affected by short-duration non-Gaussian noise transients, commonly referred to as glitches, which can obscure astrophysical signals and complicate downstream analyses. While recent work has demonstrated the effectiveness of deep learning models for glitch classification using image-based time-frequency representations, comparatively less attention has been given to systematic evaluations of machine-learning architectures operating directly on tabular glitch metadata. In this work, we present a comprehensive benchmark of classical and deep learning models for multiclass glitch classification using numerical features derived from the Gravity Spy dataset. We compare gradient-boosted decision trees with a diverse set of neural architectures, including multilayer perceptrons, attention-based models, and neural decision ensembles, and evaluate them in terms of classification performance, inference efficiency, parameter efficiency, data-scaling behavior, and cross-model interpretability alignment. We find that while tree-based methods remain strong baselines for tabular data, several deep learning models achieve competitive performance with substantially fewer parameters and exhibit distinct inductive biases and scaling behavior. A cross-model attribution analysis further reveals partially consistent feature-importance hierarchies across architectures, providing new insight into interpretability structure in tabular models. These results clarify trade-offs between performance, complexity, data efficiency, and interpretability in tabular gravitational-wave analyses and provide practical guidance for deploying machine-learning models in detector characterization pipelines.