Hi Roman,

I think that in the case of DY+jets you are correct, because there are no categories with enough statistics to use the shape we are only applying a rate uncertainty, and this is lumped in and covered by the 100% uncertainty on zj_rate. Is the 100% uncertainty enough or should we increase it?

On the other hand, for the scale and matching uncertainties for W+jets we do have some categories where the shape change can be used as a theta templates. Some of the categories don’t have enough for the shape template, but we can still say something about how the systematic affects the overall rate. This is done by removing the ‘bad’ templates and replacing them with the flat uncertainties on the lines I added. I think that this is more correct than adding the uncertainty in quadrature with the overall rate because the uncertainty should be correlated across all the categories, meaning the shift in the rate of the TopTag categories due to the scale uncertainty should be correlated with the shift in the rate in the NoTopTag categories…and if the shape in the NoTopTag categories scale systematic gives some constraint to nuissance parameter, this constraint should be propagated to the TopTag categories. I think this is done in theta because the variables have the same name so it should be the same nuissance parameter, but I am not 100% sure this is how theta works.


On May 13, 2014, at 6:02 AM, Roman Kogler <notifications@github.com> wrote:

> In theta/analysis.py:
> 
> > -    
> > +        #For categories with low statistics, use flat uncertainties instead of shape
> > +        #  Template for the following lines:
> > +        #    model.add_asymmetric_lognormal_uncertainty(sys, -math.log( _MINUS_ ), math.log( _PLUS_ ), proc, obs)
> > +        #  Where _PLUS_ = proc_sys_plus.Integral() / proc.Integral()  after the kinematic selection, before Chi2
> > +        #  Where _MINUS_ = proc_sys_minus.Integral() / proc.Integral()  after the kinematic selection, before Chi2
> > +        for obs in ['el_1top0btag_mttbar','el_1top1btag_mttbar','el_1top2btag_mttbar','el_0top2btag_mttbar']:
> > +            model.add_asymmetric_lognormal_uncertainty('scale_vjets', -math.log(1.49389), math.log(0.50611), 'wb', obs)
> > +            model.add_asymmetric_lognormal_uncertainty('matching_vjets', -math.log(1.01370), math.log(0.98630), 'wb', obs)
> > +
> > +            model.add_asymmetric_lognormal_uncertainty('scale_vjets', -math.log(1.49901), math.log(0.50099), 'wc', obs)
> > +            model.add_asymmetric_lognormal_uncertainty('matching_vjets', -math.log(0.98011), math.log(1.01989), 'wc', obs)
> > +
> > +            model.add_asymmetric_lognormal_uncertainty('scale_vjets', -math.log(1.50848), math.log(0.49152), 'wlight', obs)
> > +            model.add_asymmetric_lognormal_uncertainty('matching_vjets', -math.log(0.97089), math.log(1.02911), 'wlight', obs)
> > +
> I don't think we should add an additional nuisance parameter for the scale and matching uncertainties for W+jets and DY+jets. We would just end up with a number of highly correlated nuisance parameters. The uncertainties due to the scale and matching can be added to the rate uncertainty in quadrature.
> 
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