Dear Experts,
I’m following up on this answer given by @StephanH in this post Manipulate the likelihood.
To include a systematic constraint on the signal yield, nsig, the proposed solution was
sigScaler = ROOT.RooRealVar("sigScaler", "sigScaler", 1, 0, 2)
nSig_scaled = ROOT.RooProduct("name", "title", nsig, sigScaler)
RooAddPdf model(..., ..., RooArgSet(sig, bkg), RooArgSet(nSig_scaled, nBkg))
pdf_gaus_const = ROOT.RooGaussian('pdf_gaus_const', 'pdf_gaus_const', sigScaler, ROOT.RooFit.RooConst(1), ROOT.RooFit.RooConst(0.1))
pdf_with_const = ROOT.RooProdPdf('pdf_with_const', 'pdf_with_const', model, pdf_gaus_const)
Within the context of the original problem posted the signal yield nsig was 10 \pm 2 (stat.) events but with an additional systematic error of 1 event (10%) that had to be added, and the above was the proposed solution. It’s not clear if @Kecksdose accepted the answer as there was no follow up. But my question is about the parameterization of the constraint Gaussian. Specifically

For
sigScaler = ROOT.RooRealVar("sigScaler", "sigScaler", 1, 0, 2)
, is the initial value given by the additional 1 event systematic, and the min and max values given by the original statistical error? If yes, how is sigScalar parameterized if using asymmetric errors? If not, what is the general method of parameterizing this variable? 
For
pdf_gaus_const = ROOT.RooGaussian('pdf_gaus_const', 'pdf_gaus_const', sigScaler, ROOT.RooFit.RooConst(1), ROOT.RooFit.RooConst(0.1))
, is the constant mean the given systematic error and the constant width the error as a fraction of nsig? If true is this a good general prescription for including constraints on nsig in this way?
Thanks.