Interpretable Deep Learning for Pulsar Star Classification with Explainable AI Techniques: A Comparative Analysis of TabNet and FT-Transformer Models

The classification of pulsar stars from vast astronomical datasets is an important task in astrophysics, which aids in the discovery of these rapidly rotating neutron stars that emit periodic radio signals. Traditional machine learning techniques have demonstrated effectiveness in pulsar detection but often can lack the capacity to handle complex feature interactions within structured data. Deep learning models, especially designed for tabular data, have emerged as promising alternatives. In this study, TabNet and FT-Transformer, two advanced deep learning architectures specifically designed for tabular data are explored to enhance pulsar star classification on the HTRU2 dataset. Additionally, to improve model transparency, two explainable artificial intelligence (XAI) techniques, which are SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations), are integrated to analysis both global and local feature contributions in the classification process.