Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations

Accurate decomposition of methanol maser spectra is essential for understanding high-mass star-forming regions, especially in complex blended spectra where small differences alter physical interpretation. Conventional Gaussian fitting often fails to capture non-Gaussian structure and lacks uncertainty quantification. We develop a Bayesian spectral decomposition framework using Gaussian, Lorentzian, and Voigt profiles with Markov Chain Monte Carlo sampling, enabling model comparison and uncertainty estimation. Applied to the 6.7,GHz methanol maser G339.884$-$1.259 observed with the Ghana Radio Astronomy Observatory, our method reveals seven velocity-coherent components. The Voigt model is statistically preferred, yielding the lowest AIC and BIC ($\approx 1.98\times {10}^4$ and $1.99\times {10}^4$), the smallest RMSE ($\approx 11.1$ Jy), and the highest $R^2$ (0.985). Purely Gaussian or Lorentzian models leave systematic residuals. Elevated reduced ${\chi }_{\nu }^2$ values indicate unresolved substructure and non-ideal noise. Bayesian inference provides a robust framework for maser spectral analysis, extendable to other molecular lines and combinable with high-resolution interferometry.