MSSPE: Multi-Source Stellar Parameter Estimation under Incomplete Photometric Conditions

Abstract Accurate determination of stellar parameters is fundamental to astrophysical research, as it underpins our understanding of stellar evolution and characteristics. Most existing studies rely primarily on spectroscopic observations, which typically provide higher information density than photometric data. Although multimodal approaches combining spectra and photometry have been explored, they typically require both modalities to be simultaneously available—a condition rarely satisfied in practice—thus limiting their applicability. This study introduces Multi-Source Stellar Parameter Estimation (MSSPE), a model designed to efficiently integrate spectroscopic and photometric data while maintaining robustness under incomplete modality conditions. MSSPE consists of three key modules: (1) a feature extraction module for independent processing of spectra, photometric images, and magnitude data; (2) a feature fusion module for integrating representations to improve prediction accuracy; and (3) a reconstruction module that facilitates feature fusion and compensates for missing photometric information. Experimental results demonstrate that MSSPE substantially improves the accuracy of stellar parameter estimation, particularly for key parameters such as effective temperature and surface gravity. Incorporating Sloan Digital Sky Survey and Wide-field Infrared Survey Explorer photometric data yields a remarkable reduction in mean absolute error (MAE), especially under low signal-to-noise ratio spectroscopic conditions. Even when some photometric inputs are unavailable, the model maintains robust performance, highlighting its effectiveness in handling missing data. Overall, MSSPE effectively integrates multisource astronomical data and shows significant potential to enhance both the accuracy and reliability of stellar parameter estimation. All source codes and related materials have been publicly released at https://github.com/qintianjian-lab/MSSPE .