I’ve followed the RooFit/RooStats tutorials and have some code that fits a signal and background PDF to data using an extended unbinned maximum likelihood fit. One of the parameters it returns is the yield on the number of signal events, nsig. I take that fit and find the 90% confidence level upper limit on nsig with BayesianCalculator and use this to determine an upper limit on a branching ratio. This has worked well in one channel, but now I want to combine information from two channels to find an upper limit. I’ve seen that I can use RooSimultaneousPdf, but I do not know what to do after I’ve combined the channels.
I know that in both channels the value of nsig is proportional to the branching ratio. Can I somehow write an extended maximum likelihood function in terms of the branching ratio (instead of nsig) and then use the branching ratio as my parameter of interest in the BayesianCalculator? Is there some way of creating another RooRealVar after the fit procedure, making it a function of nsig, and using that new variable in the BayesianCalculator? Does RooFit have better ways of determining a combined upper limit with the BayesianCalculator that I just haven’t thought of?
I’d like to avoid methods that require the generation of large sets of Toy MC.