each file exists as a python script and a jupyter notebook
- intro.py
- generate toy example
- create model dict
- look at model some specs
- do simple fit
- draw profile likelihood
- compare 3 different background normalisations (fixed, constrained, free)
- fit.py
- nuisance parameters and auxdata
- generate toys
- pull distributions
- significance and upper limits
- systematics.py
- luminosity modifier
- correlated shape modifier
- uncorrelated shape modifier
- toy study for model with wrong correlation assumption
- model partially correlated uncertainties (eigendecomposition)
- splitting uncertainties by systematic source
- ratio.py
- create toy MC and data for a second channel
- fit independent signal strength in channel 2, share background normalisation parameter
- rescale signal strength to correspond to BFs
- compute ratio of correlated BFs
- extract ratio directly as POI from the fit