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 the Gallery of Examples).

CIAO 4.15

Sherpa version for CIAO 4.15 was released on December 13, 2022. Sherpa in CIAO runs under Python 3.10, 3.9, 3.8 (when installed using the conda package manager) and Python 3.9 (when installed with ciao-install). The full list of the Sherpa updates is given in the 4.15.0 and 4.14.1 release notes on GitHub. The major updates were made to data validation, plotting backend and infrastructure. The release highlights related to UI include:

XSPEC

Updated to version 12.12.1 of the XSPEC model library.

The chi2xspecvar statistics in fitting PHA data now matches XSPEC when the background is subtracted and the source or background group contains 0 counts.

Models

Models can now be frozen or thawed when accessing the model objects directly.

Filtering and grouping

Change to the filter is reported to the screen in the interactive session when using notice or ignore - including the variants notice_id and ignore2d

Plotting improvements

CIAO 4.15 continued the improvements to the plotting functionality, and includes internal changes to the plotting code and documentation.

Documentation

The Sherpa ahelp files have been updated to match the Python docstrings. Each command has its own ahelp file, rather than combining multiple commands into a single file.

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;
visualize the data with Matplotlib and DS9;
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. 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.

If you have ideas about how to enhance or improve Sherpa, please contribute ideas (and code) to the Sherpa GitHub repository.

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

Citing Sherpa in a Publication

The sherpa.citation method will return infromation from the latest release on Zenodo:

sherpa> sherpa.citation('latest')

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 2009)
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),
B. Refsdal, S. Doe, D. Nguyen, A. Siemiginowska, V. Kashyap
Proceedings of the 19th Python in Science Conference (SciPy 2011), S. van der Walt, J. Millman (Eds.), pp. 4-10 2011

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