Measurement of the ttbar Cross Section in the Lepton + Jets Channel Using Event Kinematics and NN
(Update to 347 pb-1)


R.Hughes, B. Kilminster, K. Lannon,  B. Winer
The Ohio State University

E. Thomson
University of Pennsylvania

  R. Roser
Fermi National Accelerator Laboratory

J. Conway, R. Erbacher
University of California-Davis


CDF public conference note

Final Results for 347pb-1:


Final Result
Table showing number of events in data, ttbar fraction, and resulting cross section with statistical errors listed first, systematic errors listed second. (PPT)

Final Fits to NN Output Shape:

Fits for a 347-1 pb data sample follow bellow.  The W-like shapes include the contribution from W+3p, Wbbar+1p, Z->ll (3 flavors), W->tau nu+2p, WW+1p, WZ, single top.  Electrons and muons have been combined for the fits.  The QCD shape is taken from the non-isolated leptons and fixed to 4.6% from our MET v. Isolation calculations.  The ttbar and W-like shapes float in the fits.  Based on apriori sensitivity expectations, our primary result is for the 3 or more jet bin.

W + 3 or More Jet Sample

W + 4 or More Jet Sample
W+ >= 3 jets fit
Fit to the NN-output shape for at least 3 jets, showing shapes of contributing components (EPS)
W+ >= 4 jets fit
Fit to the NN-output shape for at least 4 jets, showing shapes of contributing components (EPS)
NN Template
NN fit templates normalized to unit area (EPS)
NN Template
NN fit templates normalized to unit area (EPS)
NN Predictions
  Predicted NN Output shape for ttbar cross section of 6.1 pb and
mass = 178 GeV, showing stacked contributions (EPS)		 NN Predictions
Predicted NN Output shape for ttbar cross section of 6.1 pb and
mass = 178 GeV, showing stacked contributions (EPS)

Mass Dependence

Mass Dependence
The cross section we quote assumes a top mass of 178 GeV.  The figure above show the dependence of this result on top mass (EPS)

Neural Network Output for the Double b-Tagged Sample

B-Tagged NN Output
The output of the neural net for the double b-tagged sample in data compared to the neural net output for the double b-tagged sample in PYTHIA signal Monte Carlo.  The plots are compared with equal normalization for comparison purposes only.  This is not a fit. (EPS)

The NN-output shapes:

The NNs are trained to separate signal from background in the W+3 or More and respectively W+4 or More jet samples. In both cases we use feed forward NN with 7 input variables, 7 hidden nodes and 1 output in the [0,1] range. The input variables are: Aplanarity, Max-Jet-Rapidity, Ht, EtJ_{345}, SumPz/SumEt, Min-Dijet-Mass, Min-Dijet-Separation. The shapes of the NN output in a balanced set of PYTHIA ttbar and ALPGEN W+3(4)p events are shown bellow.

The output shape for a NN trained in the NJ>=3 mode. A balanced set of ttbar/W+3p MC events was used to fill these histograms.(EPS)
The output shape for a NN trained in the NJ>=4 mode. A balanced set of ttbar/W+4p MC events was used to fill these histograms.(EPS)

Fit Fractional-Error/Pull-Dist from pseudoexperiments:

The fit Fractional Error distribution and the fit Pull Distribution for 1500 pseudoexperiments is shown bellow. The red arrow indicates the fractional error obtained from fitting the data.

Distribution of Fit Fractional-Error for 1500 pseudo-experiments (EPS).
Distribution of Fit Fractional-Error for 1500 pseudo-experiments(EPS).

W+jets vs ttbar comparisions :

Pythia ttbar compared to Alpgen W+3p or W+4p MC for all the variables used in the NN input.

PYTHIA ttbar vs ALPGEN + HERWIG W + 3p
 PYTHIA ttbar vs ALPGEN + HERWIG W + 4p
(EPS).
 (EPS).
(EPS).
 (EPS).
(EPS).
 (EPS).
(EPS).
 (EPS).
(EPS).
 (EPS).
(EPS).
 (EPS).
(EPS).
 (EPS).
Last modified on 07/16/05 by Kevin Lannon