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PCAT (Probabilistic Cataloger) samples from the posterior distribution of a metamodel, i.e., union of models with different dimensionality, to compare the models. This is achieved via transdimensional proposals such as births, deaths, splits and merges in addition to the within-model proposals. This method avoids noisy estimates of the Bayesian evidence that may not reliably distinguish models when sampling from the posterior probability distribution of each model.
The code has been applied in two different subfields of astronomy: high energy photometry, where transdimensional elements are gamma-ray point sources; and strong lensing, where light-deflecting dark matter subhalos take the role of transdimensional elements.
We present allesfitter, a tool for flexible and robust inference of stars and exoplanets given photometric and radial velocity (RV) data. allesfitter offers a rich selection of orbital and transit models, accommodating multiple exoplanets, multi-star systems, star spots, stellar flares, and various noise models. It features both parameter estimation and model selection. A graphical user interface allows to specify input parameters, and to easily run a nested sampling or Markov Chain Monte Carlo (MCMC) fit, producing publication-ready tables, LaTex code, and plots. For all this, allesfitter provides an inference framework that unites the versatile packages ellc (light curve and RV models; Maxted 2016), aflare (flare model; Davenport et al. 2014), dynesty (static and dynamic nested sampling; https://github.com/joshspeagle/dynesty), emcee (MCMC sampling; Foreman-Mackey et al. 2013) and celerite (Gaussian Process models; Foreman-Mackey et al. 2017). The code is publicly available at https://github.com/MNGuenther/allesfitter.