https://github.com/psteinb/zbfitter

Template Fit Package to perform an extended likelihood fit on binned data for n template distributions
https://github.com/psteinb/zbfitter

Last synced: 7 days ago
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Template Fit Package to perform an extended likelihood fit on binned data for n template distributions

• Host: GitHub
• URL: https://github.com/psteinb/zbfitter
• Owner: psteinb
• License: other
• Created: 2012-02-23T13:14:17.000Z (almost 13 years ago)
• Default Branch: master
• Last Pushed: 2012-02-23T13:44:22.000Z (almost 13 years ago)
• Last Synced: 2023-03-22T18:55:19.550Z (almost 2 years ago)
• Language: C++
• Homepage:
• Size: 3.96 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README
  • License: LICENSE

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README

        
Before you start, try to contact me ([email protected]) in order to discuss if you really want to use my software (it was never written to be used publically).

0) To setup the package

   * setup root

   * setup intel threading building blocks (tbb)

   * source setupPATHS.sh in the parent directory of the package

   * issue

     $> make && make runners

     from the top directory

1) run the fits

* source prepare.sh to create environment files as in $SRC/results/central/sherpa/SV0/*.sh

* see $SRC/results/central/*.sh 

for a fit on 3 templates plus the fixed top background taken from Sherpa_XXX.root

* if goodness-of-fit information is needed, run the fit with options -P  (and -m 1) and the respective root file from a pseudo experiment (this contains the maxLLH distribution to

calculate the p-value from)

2) run pseudo experiments

* source prepare.sh to create environment files as in $SRC/results/ensemble/sherpa/SV0/

* see e.g. $SRC/results/ensemble/sherpa/SV0/do_*.sh

  for a fit on 3 templates plus the fixed top background taken from Sherpa_XXX.root

3) systematics

* generate the input files as documented in the respective directory $SRC/bundles/sherpa/SV0/systematics

* perform the fits as documented in $SRC/results/systematics

4) run linearity tests

see $SRC/results/linear/

* due to technical reasons there are 2 approaches implemented 

--> generate the input files using n variants of a given distribution

--> conduct pseudo experiments with all n variants

--> collect the mean and the average fit uncertainty

5) Have Fun!