SuperPy is a Python code that scans the CMSSM's parameter space to find the regions that best agree with experimental data with Bayesian statistics.

1. Draws a model from the CMSSM with your priors, with MultiNest.
2. Calculates predictions for the model with SoftSUSY, SuperISO, microMEGAs and FeynHiggs.
3. Calculates the chi-squared by comparing these predictions with experimental data.
4. Returns this chi-squared to MultiNest, which explores the parameter space with an efficient algorithm.

With SuperPy, it is easy to modify the likelihoods, priors or model.

SuperPlot is a GUI that plots SuperPy, SuperBayeS (with its information file format) or generally MultiNest results. It can calculate and plot:

• One- and two-dimensional marginalised posterior pdf and credible regions.
• One- and two-dimensional marginalised profile likelihood and confidence intervals.
• Best-fit points.
• Posterior means.
• Three-dimensional scatter plots.

From within the SuperPy directory,

  make all

or, for only the SuperPlot routines,

  make python

You might need to install the matplotlib Python plotting library.

From within the /SuperPy directory,

 python SuperPy.py

Alter the settings in

• Likelihoods in Likelihood.py
• Priors in Prior.py
• Scanning options in MNOptions.py

From within the SuperPy/SuperPlot sub-directory,

 python SuperGUI.py

A GUI window will appear, to select a chain. Select e.g. the .txt file in the /SuperPy/examples sub-directory. A second GUI window will appear to select an information file. Select e.g. the *.info file in the /examples sub-directory. Finally, select the variables and the plot type in the resulting GUI, and click Make Plot.