Multi-core Markov-chain Monte Carlo (MC3)
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.
Find the full MC3 documentation
at http://pcubillos.github.io/MCcubed. See
Cubillos et al. (2016) for further details.
ISM Absorption Correction
This code derives the correction to the stellar activity parameters S
and logR' caused by absorption from the interstellar medium.
is available in both IDL and Python here.
See Fossati et al., submitted for further details.
Atmospheric Modeling and Retrieval
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.
BART is available at
https://github.com/exosports/BART under a Reproducible Research
license. See Cubillos (2016) and Blecic (2016) for further details.
Space Research Institute, Austrian Academy of Sciences. Schmiedlstrasse 6, 8042 Graz, Austria.
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