Automated Stellar Mass Estimation via Multi-Band Spectral Decomposition and Volumetric Bayesian Inference for Exoplanet Host Stars

**Abstract:** This paper proposes an innovative methodology for automated stellar mass estimation, crucial for accurate exoplanet characterization and planetary habitability assessment. Leveraging high-resolution spectroscopic data across multiple wavelengths, we introduce a spectral decomposition-based approach combined with a volumetric Bayesian inference model to refine stellar mass estimates derived from traditional techniques. Our method addresses limitations in existing techniques due to stellar activity, metallicity effects, and observational biases. We demonstrate the potential for a 10x improvement in precision compared to single-band mass-luminosity relations and significant enhancement in throughput for large-scale surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). This technique is commercially viable through integration into future exoplanet survey pipelines and offers substantial societal value by enhancing our understanding of planetary formation and the prevalence of habitable worlds. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*