Likelihood fit for Poisson errors

Talk for today is here.

Sorry it doesn't look pretty. The error bar colours are particularly garish.

Muon selection with multivariate analysis

I have the steps to do the muon selection with TMVA more or less documented in my wiki page .

Meeting Minutes and Plans

Today's Meeting Minutes

Attending: Roberval, Hajrah, Mark, Joel

• Mark has been working on the final fitter and showed some plots from pseudo experiments. There are clearly still a few issues to be worked out.
• Roberval and Hajrah have been making good progress on the muon channel. They now use the ROOT package TMVA to perform a multivariate muon selection. They have also been playing around with the fit, including fitting background+gg as a single template.

Preparations for the LOI were discussed. The timescale is short, although uncertain and needs to be checked. The plan is:

1. Roberval will re-visit the event selection, and a common one should be used for the two analyses
2. Mark will continue his fit studies, and the fits from the two channels will be done independently and combined
3. Joel will look at whether the Z-fusion eeH process is an issue.
4. It is uncertain whether combing background+gg templates is the best thing to do, so it will need to be studied

We will have extra meetings in the run up to the LOI, starting next week (at 11:30)

Status of the analysis

Talk can be downloaded from here .

Toy MC fit

I had a quick go at a toy MC fit, needs a bit more work though.

First I created two toy data sets from the Poisson distribution of some input histograms, and then tried fitting them together using the error on both to calculate the chi2. First results are here. The gluon fit seems to be a bit erratic for some reason.

I then tried fitting one of the toy sets to the input using only the error on one of them. The results are here. Something's clearly going wrong here, I'm looking in to it but probably won't have time before the meeting at 11. The results are so bad it's got to be something obvious though.

In both sets of plots the black line is the true value of the ratio.

Neural networks

I've been trying to understand the differences that Mark found between the results using SGV-Tesla and Mokka-LDCPrime_02Sc neural networks. I am pretty confident that the new neural networks is better than the old ones. Not only because other plots, such as the purity-efficiency, say they present better performance, but becasue the old ones are not correct as some overtraining can be spotted.
I wrote my conclusions on neural networks down in the web page here.

Purity x Efficiency (Z-Higgs)

Here is the flavour tag purity-efficiency plot with Z-Higgs events comparing Mokka-LDCPrime_02Sc neural networks (open symbol) and SGV-Tesla neural networks (solid symbol).
The c-tag performance is similar, but slightly better for Mokka-LDCPrime_02Sc, for mid to high efficiencies. Mokka-LDCPrime_02Sc is much better for low efficiencies.