Abstract: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 $\chi^{2}_{\nu}$ 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.
From: Theophilus Ansah-Narh [view email]
[v1]
Sat, 30 May 2026 15:19:31 UTC (2,029 KB)
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