Measured WZ Diboson Cross Section with 5.9/fb at CDF

• Author: Jason Nett
• Blessed: 27 May 2010
• Data: CDF Run II 5.9/fb in events with three high-pt leptons

Table of Contents

• Abstract
• Event Count
• Summary of Signal Region
• Basic Kinematics
• Neural Network Input Variables
• Neural Network Score
• Summary of the Low Missing E_T Control Region
• Basic Kinematics
• Neural Network Input Variables
• Neural Network Score
• Summary of the Z-Removed Control Region
• Basic Kinematics
• Neural Network Input Variables
• Neural Network Score

Abstract

WZ production is a crucial background to the H→WW search in the associated production mode decaying to a three-lepton signature plus missing E_T signature. We are updating the Standard Model WZ cross section measurement to that found with 5.9 fb^-1 from the previous measurement using 1.9 fb^-1 in the three-lepton plus missing E_T signature. A NeuroBayes neural network is used to distinguish WZ signal from background processes in the final selection region. The WZ cross section is then extracted using a maximum likelihood method which best fits the neural network score distribution to the data. This analysis measures the WZ cross section to be 3.7±0.8 picobarns (or 3.7±0.6(stat.). +0.6-0.4 (syst.)) which is in agreement with the 3.46±0.21 picobarns predicted by theory. For details, see the public note for this measurement.

Event Count

Requirements For All Regions

• Exactly three leptons (i.e. electrons or muons, or if one of the three leptons is a tau and the tau then decays leptonically
• Leading lepton p_T>20.0 GeV
• Second and third lepton p_T>10.0 GeV
• Any number of jets are acceptable

Additional Requirements For Signal Region

• Missing E_T>25.0 GeV
• Z-Selection: Each event has three leptons and so three possible pairings of leptons. If an event has at least one lepton pairing passing the following criteria, then it is accepted into the signal region
• opposite electric charge
• same lepton flavor
• a dilepton invariant mass within [ 76.0, 106.0] GeV

Additional Requirements For Low Missing E_T Control Region

• Missing E_T<20.0 GeV
• Z-Selection: Each event has three leptons and so three possible pairings of leptons. If an event has at least one lepton pairing passing the following criteria, then it is accepted into the low missing E_T control region
• opposite electric charge
• same lepton flavor
• a dilepton invariant mass within [ 76.0, 106.0] GeV

Additional Requirements For Z-Removed Control Region

• No E_T cut
• Z-Rejection: Each event has three leptons and so three possible pairings of leptons. If an event has at least one lepton pairing passing the following criteria, then it is accepted into the Z-peak removed control region
• opposite electric charge
• same lepton flavor
• a dilepton invariant mass NOT within [ 66.0, 116.0] GeV

Summary of Signal Region

Basic Kinematic Distributions

Leading lepton p_T

2nd lepton p_T

3rd lepton p_T

Missing E_T

Number of Jets

Reconstructed Z-boson Mass

Reconstructed Transverse Mass of the W-boson

Lepton Flavor Combinations of the three leptons

Neural Network Input Distributions

Once we have a set of data (and monte carlo) passing all analysis cuts, we could simply calculate the measured cross section based on the total count of the data minus the total count of the expected background (according to monte carlo simulation), but several techniques have been developed to more optimally distinguish the signal from background and achieve a better result from the same amount of collected data.

One such technique is a neural network, of which this analysis employs the NeuroBayes incarnation. A neural network is a computational tool that inputs several distributions that show separated, though usually overlapping, signal and background curves. It then outputs a single distribution that ideally will exhibit a larger separation between the signal and background curves than any one of the inputs does.

Some neural net input distributions are more effective at discriminating between the signal and background curves than other. The following is a list of the input variables used in this analysis, ranked from most to least significant as reported by NeuroBayes:

1. Δφ(W-Lepton, Missing E_T) -- The azimuthal angle between the W-Lepton and Missing E_T
2. m_T (jets) -- The transverse mass of the jets. Events that have zero jets are assigned a value of zero.
3. Lepton Type Combinations -- Discriminate among events that have eee (all three leptons are electrons), eeμ, eμμ, etc.
4. Missing E_T
5. m_T(W-Lepton, Missing E_T)
6. Δφ(2nd lepton,Missing E_T)
7. H_T--The sum of transverse components of all leptons, jets, and missing E_T
8. m_T(all leptons, jets, and missing E_T)
9. m_T(3rd lepton,missing E_T)
10. Δφ(vector sum of the three leptons,missing E_T)
11. m_T(the three leptons)
12. Number of jets

Δφ(W-Lepton, Missing E_T)

m_T (jets)

Δφ(2nd lepton,Missing E_T)

H_T

m_T(all leptons, jets, and missing E_T)

m_T(3rd lepton,missing E_T)

Δφ(vector sum of the three leptons,missing E_T)

m_T(the three leptons)

Neural Network Score

Here is the neural network score--the output of the neural network from which a likelihood function fit method is used to extract the WZ measured cross section.

Signal Region Neural Network Score

Summary of the Low Missing E_T Control Region

Basic Kinematic Distributions

2nd lepton p_T

3rd lepton p_T

Missing E_T

Number of Jets

Reconstructed Z-boson Mass

Reconstructed Transverse Mass of the W-boson

Lepton Flavor Combinations of the three leptons

Neural Network Input Distributions

Δφ(W-Lepton, Missing E_T)

m_T (jets)

Δφ(2nd lepton,Missing E_T)

H_T

m_T(all leptons, jets, and missing E_T)

m_T(3rd lepton,missing E_T)

Δφ(vector sum of the three leptons,missing E_T)

m_T(the three leptons)

Neural Network Score

Low Missing E_T Region Neural Network Score

Summary of the Z-Removed Control Region

Basic Kinematic Distributions

2nd lepton p_T

3rd lepton p_T

Missing E_T

Number of Jets

Lepton Flavor Combinations of the three leptons

Neural Network Input Distributions

m_T (jets)

Δφ(2nd lepton,Missing E_T)

H_T

m_T(all leptons, jets, and missing E_T)

m_T(3rd lepton,missing E_T)

Δφ(vector sum of the three leptons,missing E_T)

m_T(the three leptons)

Neural Network Score

Z-Removed Region Neural Network Score