MC3 is a powerful Python Bayesian-statistics tool to perform Levenberg-Marquardt least-squares optimization and Markov-chain Monte Carlo (MCMC) posterior-distribution sampling. MC3 runs from the Shell prompt or through the Python interpreter, supports non-informative or Gaussian priors, and provides correlated-noise estimation with the Time-averaging or the Wavelet-based Likelihood methods.
This code derives the correction to the stellar activity parameters S and logR' caused by absorption from the interstellar medium.
The Bayesian Atmospheric Radiative Transfer project (BART) consists of three independent modules to compute radiative transfer, thermochemical equilibrium abundances (TEA), and a Bayesian MCMC sampler (MC3). The BART code can compute forward-modeling exoplanet emission and transmission spectra including line-by-line, cross-section, alkali, Rayleigh, and cloud opacities. BART also works in a retrieval configuration to constrain the temperature and composition of exoplanet atmospheres upon comparing the theoretical spectra with observed eclipse or transit data.
The Repack Python package (Cubillos 2017) compresses line-by-line transition opacity databases from Exomol, HITEMP, and Kurucz TiO, enabling faster radiative-transfer computations without loss of information. To do so, Repack preserves the full LBL information of only the strong lines that dominate the spectrum, and compressed the opacity from the weak lines into a continuum opacity. The compressed dataset reproduces ~99% of the original opacity, taking into considertion the temperature dependence of the opacity.
BThe Repack code is available at https://github.com/pcubillos/repack.