Automated Stellar Parameter Estimation through Multi-Modal Bayesian Inference and Adaptive Kernel Regression

**Abstract:** This paper presents a novel approach for accurately and efficiently estimating stellar parameters (effective temperature, surface gravity, metallicity, and rotational velocity) from a combination of photometric and spectroscopic data. We leverage a multi-modal, Bayesian inference framework integrated with an adaptive kernel regression technique to overcome the limitations of traditional methods which often struggle with data sparsity and complex parameter correlations. This system surpasses existing techniques in speed and accuracy and poses a readily commercializable alternative to labor-intensive traditional methods. Potential applications include large-scale stellar surveys, exoplanet host star characterization, and transit photometry analysis.