analysis_v1

Creates a named analysis, i.e. a triplet of theory, data and covariance matrix. The covariance matrix may be diagonal and contain only statistical uncertainties or contain a systematic part as well.

Options

• -n, --name – defines the name of the analysis (required)

• -d, --datasets – defines the list of datasets to use for the analysis (required)

• -p, --cov-parameters – defines the parameters for the covariance matrix

• --cov-strict – raises an exception if a parameter is skipped because it does not affect the output

• -o, --observable – defines the observable (model) to be fitted

• --toymc – use random sampling to variate the data/theory

  • choices: covariance, poisson, normal, normalStats, asimov

• --toymc-source – defines the source for ‘–toymc’ option

  • choices: data or theory

  • default: theory

• --covariance-updater – defines name of the hook that triggers a covariance matrix update

• -v, --verbose – define verbosity level

Examples

• Initialize an analysis ‘analysis’ with a dataset ‘peak’:

  ./gna \
      -- gaussianpeak --name peak_MC --nbins 50 \
      -- gaussianpeak --name peak_f  --nbins 50 \
      -- ns --name peak_MC --print \
          --set E0             values=2    fixed \
          --set Width          values=0.5  fixed \
          --set Mu             values=2000 fixed \
          --set BackgroundRate values=1000 fixed \
      -- ns --name peak_f --print \
          --set E0             values=2.5  relsigma=0.2 \
          --set Width          values=0.3  relsigma=0.2 \
          --set Mu             values=1500 relsigma=0.25 \
          --set BackgroundRate values=1100 relsigma=0.25 \
      -- dataset-v1  --name peak --theory-data peak_f.spectrum peak_MC.spectrum -v \
      -- analysis-v1 --name analysis --datasets peak -v
  

• Initialize an analysis ‘analysis’ with a dataset ‘peak’ and covariance matrix based on constrained parameters:

  ./gna \
      -- gaussianpeak --name peak_MC --nbins 50 \
      -- gaussianpeak --name peak_f  --nbins 50 \
      -- ns --name peak_MC --print \
          --set E0             values=2    fixed \
          --set Width          values=0.5  fixed \
          --set Mu             values=2000 fixed \
          --set BackgroundRate values=1000 fixed \
      -- ns --name peak_f --print \
          --set E0             values=2.5  relsigma=0.2 \
          --set Width          values=0.3  relsigma=0.2 \
          --set Mu             values=1500 relsigma=0.25 \
          --set BackgroundRate values=1100 relsigma=0.25 \
      -- dataset-v1 peak --theory-data peak_f.spectrum peak_MC.spectrum -v \
      -- pargroup covpars peak_f -m constrained \
      -- analysis-v1  analysis --datasets peak -p covpars -v