p17 ZH -> ll bb
Neural Net Event Selection

NN

Using ROOT TMultiLayerPerceptron class.

Input expected distributions in MC of background and signal, after requiring >=2 jets and a Z candidate.

All results are so far for muon channel, but can be easily extended to electrons as well.

Currently using a fairly simple set of kinematic variables.

More variables were added, in order of significance, until significance failed to improve.

M(jj), pt1, pt2, dR(mumu),
MET, dR(jj), M(mumu), DeltaEta(jj)

Including QCD background from data as a background sample: (M(mumu) outside Z mass 70-110 window)

- Can train the NN against QCD kinematics

Separate NN for each m_H (105, 115, ... 155)

- Plots are shown for mh=115

Normalization

Data, Signal, and Total Background are given the same normalization.

Backgrounds are normalized according to their weights in the 2-tag channel.

qcd

tt,wz,zz

Z+Xlp

Z+bb+Xlp

Training: #Epochs

First, find the optimal number of training epochs

for each number of hidden neurons.

Training: #Hidden Neurons

Then find the best number of hidden neurons.

We chose 8.

Training: Method

We also tested each of the available training methods

(algorithms for back-propagation minimization).

We chose BFGS.

Outputs

Untagged

Double-tagged

Significance

Find an optimal cut on the NN that maximizes S/sqrt(B)

Measure the ratio of S/sqrt(B) for this cut, vs. no NN but with an Mjj cut of 1.5 sigma.

Have implemented a simple NN for separating signal from background in ZH->llbb

Performance will be even better once CLs is used directly on the NN output for limit setting.

To do:

Improve agreement between background MC and data (for untagged samples)...

Z pT re-weighting should help a lot

Find a way to assign a systematic uncertainty due to uncertainties in the background modeling

Optimize variables further, use smarter variables?

Loosen cuts in the analysis (lower jet pT?, lower lepton pT?)

Include the NN b-tagging output into the NN event selection?

Conclusions

Andy Haas

2004-07-07T15:59:13