A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

In the ever-evolving landscape of astrophysics and machine learning, understanding the internal kinematics of galaxies remains a formidable challenge. Traditional techniques for modeling galaxy dynamics have offered valuable insights but are often limited by their inability to capture complex, non-linear relationships in high-dimensional data. Recent advances in quantum computing and explainable artificial intelligence (XAI) provide new avenues for addressing these challenges, paving the way for more sophisticated and interpretable models in astrophysical research [19] [20] [21]. Galaxy velocity dispersion is a critical parameter that underpins our understanding of the mass distribution, dynamical state, and evolutionary history of galaxies. By analyzing detailed stellar population and kinematic properties--such as morphological classification, effective radius, and gradients in stellar age and metallicity, the prediction of velocity dispersion becomes central to characterizing the intricate interplay between a galaxy's structure and its dynamic behavior. The MaNGA dataset, with its rich set of 11 features, offers a robust platform for exploring these phenomena and highlights the technical demands of achieving accurate predictions in this domain [1].