Bayesian Framework for modeling the high energy astrophysics data has been implemented in Sherpa, a modeling and fitting application in CIAO. Sherpa is written in Python and the latest version can be installed and used with Python 2.7. We describe the concept of models with priors, the MCMC options for exploring the posterior probability distributions, and available algorithms for hypothesis testing and model selection. The methods correctly account for the Poisson nature of high energy astrophysics data from space-based X-ray and gamma-ray missions such as Chandra or Fermi. In most situations the modeling has to account for instrumental effects characterized by a probability of detecting photons of a given energy at a particular detector channel, or a particular location on the detector. We provide variety of examples based on the high energy data with ready to use recipes. Some future directions and potential linking with other Python packages will also be presented.
AAS/High Energy Astrophysics Division #13
- Pub Date:
- April 2013