A Python sampling method for obtaining cosmological constraints from SN light curves using Approximate Bayesian Computation.

Project description

Approximate Bayesian computation (ABC) and so called “likelihood free” Markov chain Monte Carlo techniques are popular methods for tackling parameter inference in scenarios where the likelihood is intractable or unknown. These methods are called likelihood free as they are free from the usual assumptions about the form of the likelihood e.g. Gaussian, as ABC aims to simulate samples from the parameter posterior distribution directly. superABC is a python package that implements the astroABC sampler using either the SNANA or the sncosmo Supernovae simulation packages and simulates samples from the posterior distribution using two distinct distance metrics. superABC requires NumPy,SciPy and astroabc. The SNANA and rootpy packages are required if using this as a forward model simulation. sncosmo, astropy and pandas are required if using sncosmo as the forward model simulation.

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