Bayesian MCMC decomposition of the G339.884-1.259 methanol maser spectrum identifies seven velocity components and statistically prefers the Voigt profile over Gaussian or Lorentzian based on AIC, BIC, RMSE, and R².
The Arecibo Methanol Maser Galactic Plane Survey-IV: Accurate Astrometry and Source Morphologies
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We present accurate absolute astrometry of 6.7 GHz methanol masers detected in the Arecibo Methanol Maser Galactic Plane Survey using MERLIN and the Expanded Very Large Array (EVLA). We estimate the absolute astrometry to be accurate to better than 15 and 80 milliarcseconds for the MERLIN and EVLA observations respectively. We also derive the morphologies of the maser emission distributions for sources stronger than ~ 1 Jy. The median spatial extent along the major axis of the regions showing maser emission is ~ 775 AU. We find a majority of methanol maser morphologies to be complex with some sources previously determined to have regular morphologies in fact being embedded within larger structures. This suggests that some maser spots do not have a compact core, which leads them being resolved in high angular resolution observations. This also casts doubt on interpretations of the origin of methanol maser emission solely based on source morphologies. We also investigate the association of methanol masers with mid-infrared emission and find very close correspondence between methanol masers and 24 micron point sources. This adds further credence to theoretical models that predict methanol masers to be pumped by warm dust emission and firmly reinforces the finding that Class II methanol masers are unambiguous tracers of embedded high-mass protostars.
fields
astro-ph.IM 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Bayesian estimation of spectral parameters of the 6.7-GHz methanol maser G339.884-1.259 from GRAO observations
Bayesian MCMC decomposition of the G339.884-1.259 methanol maser spectrum identifies seven velocity components and statistically prefers the Voigt profile over Gaussian or Lorentzian based on AIC, BIC, RMSE, and R².