MCMC module for X-ray analysis in Sherpa
Python
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README.md
funcs.py
mh.py
mhtest.py
pyblocxs.py
ui.py

README.md

pyblocxs

MCMC module for X-ray analysis using CIAO/Sherpa

Accounts for calibration uncertainty (effective areas only) using PragBays and FullBayes.

References:

  • Analysis of Energy Spectra with Low Photon Counts via Bayesian Posterior Simulation van Dyk, D.A., Connors, A., Kashyap, V.L., and Siemiginowska, A., 2001, ApJ, 548, 224
  • How to handle calibration uncertainties in high-energy Astrophysics Kashyap, V.L., Lee, H., Siemiginowska, A., McDowell, J., Rots, A., Drake, J., Ratzlaff, P., Zezas, A., Izem, R., Connors, A., van DYk, D., and Park, T., 2008, Observatory Operations: Strategis, Processes, and Systems, Eds. Roger J. Brissenden and David R. Silva, Proc. of SPIE, v7016, pp.70160P-70160P-8
  • Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting Lee, H., Kashyap, V.L., van Dyk, D.A., Connors, A., Drake, J.J., Izem, R., Meng, X.-L., Min, S., Park, T., Ratzlaff, P., Siemiginowska, A., and Zezas, A., 2011, ApJ, 731, 126
  • A Fully Bayesian Method for Jointly Fitting Instrumental Calibration and X-Ray Spectral Models Xu, J., van Dyk, D.A., Kashyap, V.L., Siemiginowska, A., Connors, A., Drake, J., Meng, X.-L., Ratzlaff, P., and Yu, Y., 2014, ApJ, 794, 97