Dense Gas Toolbox v1.3

Dense Gas Toolbox

Aim: Calculate density and temperature from observed molecular emission lines, using radiative transfer models.

Method: Our models assume that the molecular emission lines emerge from a multi-density medium rather than from a single density alone. The density distribution is assumed to be log-normal or log-normal with a power-law tail. The parameters (density, temperature and the width of density distribution) are inferred using Bayesian statistics, i.e. Markov chain Monte Carlo (MCMC).

Results: Given an ascii table of observed molecular intensities [K km/s], the results (mass-weighted mean density, temperature and width of the density distribution) are saved in an output ascii file. Furthermore, diagnostic plots are created to assess the quality of the fit/derived parameters.

THIS RELEASE

• New: The user may optionally infer the parameters (density, temperature, width of density distribution) via application of the MCMC method.

• New: Diagnosis plots (corner plots) are produced when MCMC method is used.

• Update: Code updated to Python 3.X

• Update: Re-calculation of models, now including the following transitions: 12CO (up to J=3), 13CO (up to J=3), C18O (up to J=3), C17O (up to J=3), HCN (up to J=3), HCO+ (up to J=3), HNC (up to J=3) and CS (up to J=3)