Search for WW/WZ&rarr l&nu jj at CDF using 1.2 fb-1 of data
Anna SfyrlaUniversity of Geneva
A search for WW and WZ production in lepton-neutrino plus dijet
final state is presented here. The contributing Feynman diagrams are
shown in the following figure:
The WW and WZ production has been observed so far in the fully
leptonic channel at the Tevatron. The aim of this analysis is the
observation in the semi-leptonic decay channel.
The semileptonic decay channel is, from all the diboson decay
channels, the favorable for Trilinear Gauge Coupling (TGC) measurements,
due to the big branching ratio with respect to the leptonic channel.
The event selection associated with a leptonic W consists of exactly
one tight lepton with transverse momentum > 20 GeV, missing transverse energy > 25 GeV and
lepton-missing energy transverse mass > 30 GeV. The hadronically decaying W is reconstructed
by the two leading jets of events with two or more jets. The dijet
invariant mass window considered in the analysis is [45,160] GeV.
The theoretical cross sections for this decay mode are
&sigmaWW × Br(W &rarr l &nu, W &rarr jj) = 12.4 pb ×
0.146 = 1.81 pb
&sigmaWZ × Br(W &rarr l &nu, Z &rarr jj) = 3.96 pb ×
0.07 = 0.28 pb
There are several processes that result in the same final state
topology (1 lepton, electron or muon, missing transverse energy and jets) as the diboson production, and thus are backgrounds to this search. The background processes that are taken into account are:
- W (&rarr l &nu)+jets
- W (&rarr &tau &nu)+jets
- Z (&rarr ll)+jets
The largest of these backgrounds is the W(&rarr
l &nu)+jets. The high theoretical cross section of this process
(&sim 300 pb) leads by itself, but even more with the rest of the backgrounds, to a very poor Signal over Background ratio (S/B < 1% initially).
2. Improved Event Selection
In order to improve the initially very poor S/B, a tool with big discriminative power is
needed, and as such a Neural Network (NN) has been used, and more precisely the ROOT interface
The NN has been trained with 6 input variables. The input variables,
that are listed in the following picture (red curves for signal
and black for background), are angle- or event shape- related
variables. That way, the NN output is kept as uncorrelated as possible
with the dijet invariant mass, that is the variable on which the
analysis is based, since it provides a good description of the signal. The NN is trained using MC events for the signal and background description and uses events in the signal region only.
The NN output (picture below) is used to perform a cut at the point of
the maximum significance gain. In this case, the significance is
Significance = S/&radic(S+B)
The maximun significance gain point, is the point where a cut would
maximize the significance. This is at approximately 0.46 at the NN
Taking into account the events that correspond in the signal region of
the NN output (with the cut as defined with the significance curve),
we get an overall significance gain of &sim 10%. The gain in the signal fraction is &sim 50%.
Plotting the invariant mass distribution we get good agreement between
the MC and the data in the sidebands. Good agreement between data and
MC comes also in the NN output, using information from the sidebands (following pictures).
3. Signal and Background Parameterizations
The parametrization of the signal and background shapes after the NN
cut will be used in a likelihood fit that will be applied on data to
estimate the signal fraction. The signal shape is fixed using MC, and
the background parameterization is motivated by MC. Both are plugged
into a likelihood fitter, that has three free parameters, two related
to the background parameterization and the third is the signal
fraction. The background parameters and the signal fraction are given
by a likelihood fit to data.
The likelihood fit to data is shown in the following plot. The red
curve shows the signal plus background description, while the blue
curve shows the background only.
The background subtracted from the data is shown in the following plot. The red curve shows the signal shape for the signal fraction measured on the data (using the fitter) and the red band the statistical error of the signal fraction.
4. Results and Summary
We presented a search for WW plus WZ production in lepton-neutrino plus dijet final state, using 1.2 fb-1 of data and central tight electrons and muons in the lepton selection. A NN has been used for the significance optimization.
The theoretical prediction for the cross section is
&sigma × Br = 2.09 ± 0.14 pb
NSignal = 410 ± 212(stat) ± 107(sys)
that correspond to a cross section
× Br = 1.47 ± 0.77(stat) ± 0.38(sys) pb
The 95%CL upper limit to the cross section is estimated to
&sigma × Br < 2.88 pb