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Last modified: 11 December 2014

URL: http://cxc.harvard.edu/sherpa/index.html
[Sherpa logo]

CIAO's modeling and fitting package


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).

[New] Sherpa version 1 for CIAO 4.7 was released on December 16, 2014. This is primarily a maintenance release with several bug fixes and upgrade to the newer 12.8.2e XSPEC model library (and under the hood making use of more robust library interfaces). Sherpa now supports stacks of datasets: package for manipulating a stack of related datasets and simultaneously fitting a model to them. It provides stack-enabled (i.e. vectorized) versions of the key Sherpa commands used to load data, set source models, get and set parameters, fit, and plot.

Sherpa standalone Python binary package was released on 2 September, 2014. The Sherpa source release is planned for January 2015. See Sherpa Python page for details.

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.

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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.

Meet the Sherpa Team 2014.
[photos of the Sherpa Team]

Please send feedback and questions on Sherpa to the CXC Helpdesk.


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.


Last modified: 11 December 2014
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