Sherpa: CIAO's Modeling & Fitting Application

CIAO's modeling and fitting package

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Sherpa is the CIAO modeling and fitting application. It enables the user to construct complex models from simple definitions and fit those models to data, using a variety of statistics and optimization methods (see Gallery of Examples).

Latest Version

Sherpa version 1 for CIAO 4.8 was released on December 15, 2015. The major updates are: the update of the XSPEC models to version 12.9.0d; the addition of the wstat statistic, for including a background dataset as a model component (it is based on the XSPEC version); support for background data sets in user statistics created with load_user_stat; a major upgrade to the Python docstrings (the ahelp pages are still available in this release); and it is the first CIAO release based on the GitHub-developed version of Sherpa. Please consider contributing to Sherpa development, whether by adding code, fixing bugs, or changing documentation.

Sherpa lets you:

fit 1-D data sets (simultaneously or individually), including:
spectra, surface brightness profiles, light curves, general ASCII arrays;
fit 2-D images/surfaces in the Poisson/Gaussian regime;
access the internal data arrays;
build complex model expressions;
import and use your own models;
choose appropriate statistics for modeling Poisson or Gaussian data;
import new statistics, with priors if required by analysis;
visualize a parameter space with simulations or using 1-D/2-D cuts of
the parameter space;
calculate confidence levels on the best-fit model parameters;
choose a robust optimization method for the fit: Levenberg-Marquardt,
Nelder-Mead Simplex or Monte Carlo/Differential Evolution;
perform Bayesian analysis with Poisson Likelihood and priors, using
Metropolis or Metropolis-Hastings algorithm in the MCMC (Markov-Chain Monte Carlo);
and use Python to create complex analysis and modeling functions,
build the batch mode analysis or extend the provided functionality
to meet the required needs.

The Sherpa infrastructure greatly enhances the default Sherpa functions, and provides users with an environment for developing complex and sophisticated analysis.

Sherpa is designed for use in a variety of modes: as a user-interactive application and in batch mode. Sherpa is an importable module for the Python the scripting language and is available as a C/C++ library for software developers. In addition, users may write their own Python scripts for use in Sherpa.

The About Sherpa page outlines key features of the software, and the Latest Updates page describes new functionality and recent changes. See also the SciPy2009 conference proceedings for detailed information about Sherpa's capabilities.

Please send feedback and questions on Sherpa to the CXC Helpdesk or the Sherpa Issues list on GitHub.

Citing Sherpa in a Publication

If you are writing a paper and would like to cite Sherpa, we recommend the following papers and presentations:

Sherpa: a mission-independent data analysis application (ADS)
P. E. Freeman, S. Doe, A. Siemiginowska
SPIE Proceedings, Vol. 4477, p.76, 2001

\bibitem[Freeman et al.(2001)]{2001SPIE.4477...76F} Freeman, P., Doe, S., 
\& Siemiginowska, A.\ 2001, \procspie, 4477, 76

The specific version of CIAO and CALDB (if applicable) used for the analysis should be mentioned as well.

A reference for the Python interface to Sherpa is also available:

Developing Sherpa with Python (ADS)
S. Doe, et al.
Astronomical Data Analysis Software and Systems XVI, 376, 543

\bibitem[Doe et al.(2007)]{2007ASPC..376..543D} Doe, S., et al.\ 2007, 
Astronomical Data Analysis Software and Systems XVI, 376, 543

Sherpa: 1D/2D modeling and fitting in Python (SciPy)
B. Refsdal, S. Doe, D. Nguyen, A. Siemiginowska, N. Bonaventura, D. Burke, I. Evans, J. Evans, A. Fruscione, E. Galle, J. Houck, M. Karovska, N. Lee, M. Nowak,
Proceedings of the 8th Python in Science conference (SciPy 2009), G. Varoquaux, S. van der Walt, J. Millman (Eds.), pp. 51-57 2009

Fitting and Estimating Parameter Confidence Limits with Sherpa (SciPy 2011 presentation)
B. Refsdal, S. Doe, D. Nguyen, A. Siemiginowska, V. Kashyap

Further guidelines are available from the Acknowledgment of Use of Chandra Resources.