FFNN–TabNet: An Enhanced Stellar Age Determination Method Based on TabNet

Abstract

The precise ascertainment of stellar ages is pivotal for astrophysical research into stellar characteristics and galactic dynamics. To address the prevalent challenges of suboptimal accuracy in stellar age determination and limited proficiency in apprehending nonlinear dynamics, this study introduces an enhanced model for stellar age determination, amalgamating the Feedforward Neural Network (FFNN) with TabNet (termed FFNN–TabNet). The methodology commences with the acquisition of a stellar dataset via meticulous cross-matching. Subsequent advancements encompass refinements to the activation functions within TabNet, coupled with augmentations to the Attentive transformer module by incorporating an FFNN module. These enhancements substantially boost training efficiency and precision in age estimation while amplifying the model’s capability to decode complex nonlinear interactions. Leveraging Bayesian Optimization Algorithm (BOA) for hyperparameter fine-tuning further elevates the model’s efficiency. Comprehensive ablation and comparative analyses validate the model’s superior performance in stellar age determination, demonstrating marked enhancements in accuracy. The experiment also demonstrates an enhanced ability of the model to capture nonlinear relationships between features.