Fit with finite MC - first attempt

My first attempt to fit with finite MC samples. Results here .
Still to check:

• How to treat bins with zero entries.
• Are errors calculated correctly?
• How to fix one parameter.

Fit with finite statistics

Joel found this paper, which I think describes exactly what we need. I'm going through it at the moment trying to figure out how to apply it.

There is a root implementation, TFractionFitter, but in one dimension. That could be used by just putting everything in one dimension e.g. instead of a 10x10 bin histogram have a 100 bin histogram. I also found this page which says there are problems with the implementation (although it's unclear for which root version). One of the comments says that there is an implementation in RooFit that works properly though, so I'll have a look at that.

Event selection with TMVA

Link here .

Leptonic event selection update

Today's talk is here.

Leptonic Event Selection

Last week's talk is here.

Correct fits and proposal for more samples

I applied the corrections in the re-scaling of the errors from the templates in the chi^2 function. You can find the results here.

The results in the last column were obtained without taking into account the statistical errors from the templates. We can see that as the number of events increase the absolute value of the errors tend to the case when the errors from the templates are zero. The central values fluctuates a lot. An extra sample with L = 2000 fb^-1 (34000 events of mu-mu-h) would be the minimum (Desch-Kuhl used 5000 fb^-1 and split in data and Monte Carlo) . Presently there is 1000 fb^-1 reconstructed. We can use 500 fb^-1 (which is what one expects in reality) for the data and the other 500 fb^-1 with the extra 2000 fb^-1 that we request. I would prefer more, but we have to see that the time scale is short.

Besides that we should try to get an extra 1000 fb^-1 for likelihood or other method for signal-background separation.

What do you think?

Fit studies

A slide can be found here .