Search for Standard Model Higgs Boson Production in Association with a W Boson using Neural Networks with 2.7 -1 of CDF data
Tatsuya Masubuchi, Shinhong Kim, Yoshikazu Nagai (University of Tsukuba)
Jay Dittmann, Martin Frank Nils Krumnack (Baylor University)
Richard Hughes, Kevin Lannon, Jason Slaunwhite, Brian Winer (Ohio State University)
Anyes Taffard (UC Irvine)
Weiming Yao (LBNL)
Pedro Fernandez (FNAL)
Jason Nielsen (UC Santa Cruz)
Thomas Peiffer, Jeannine Wagner-Kuhr, Thomas Muller, Wolfgang Wagner (Universitat Karlsruhe)
Andreas Warburton, Adrian Buzatu (McGill University)

 


- Abstract -
- Event Selection -
- Input Variables -
- Neural Network Output -
- Systematics -
- Results -


Documentation: Public Analysis Note




Abstract:

We present a search for the Standard Model Higgs boson decaying to two b-quarks and produced in association with a W boson. This search uses data corresponding to an integrated luminosity of 2.7/fb. We select events with a high-pT lepton, a neutrino, and two jets. We require at least one of the jets to be identified as a b-quark jet (tagged) using two different tagging algorithms (SECVTX and JETPROB). The discrimination between the Higgs signal and the large backgrounds in the W + 2 jets bin is increased through the use of an artificial neural net. We see no evidence for an excess of Higgs signal in the NN output distribution. We set a 95% confidence level upper limit on the WH cross section times the branching ratio of the Higgs to decay to a bbbar pair, expressed as a ratio to the SM cross section:
σ (ppbar -> WH)*BR(H->bbbar) < 5.0 x SM observed (5.8 expected) at M(H) = 115 GeV/c2
σ (ppbar -> WH)*BR(H->bbbar) < 3.6 to 63 x SM for M(H) = 100 to 150 GeV/c2


Public Analysis Note




Event Selection:
We analyze 2.7 fb -1 of events recorded triggers for high pT electrons, muons, and large missing transverse energy plus 2 jets (MET + 2 Jets Trigger). We require events to have a high-pT lepton candidate, missing transverse energy, and two jets with at least one b-tag. We classify our events into 3 lepton categories: central triggered electrons and muons, forward (plug) triggered electrons, and non-triggered leptons (isolated tracks). Each lepton sample has distinct backgrounds and triggers requirements, and so has a different set of event selection cuts. The following tables summarize the cuts:
Event Selection for Lepton Triggered Events
CategoryDouble SECVTXOne SECVTX + One JetProbOne SECVTX
Lepton Central isolated electron or muon and Plug isolated electron (Pt>20 GeV)
Missing Et < 20 GeV(Central), >25 GeV(Plug)
Two Jets > 20 GeV, |&eta| < 2.0
b-tagging (1st jet)tight SecVtx b-tag
b-tagging (2nd jet)tight SecVtx b-tagJetProb b-tagNo b-tag
QCD vetoPlug electron onlyQCD Veto


Event Selection for MET+Jet triggered Events
CategoryDouble SECVTX tagOne SECVTX + One JetProbOne SECVTX tag
Lepton Isolated track (Pt>20 GeV)
Missing Et >20 GeV
Two Jets > 25 GeV, |η| < 2.0
b-tagging (1st jet)tight SecVtx b-tag
b-tagging (2nd jet)tight SecVtx b-tagJetProb b-tagNo b-tag
QCD Veto No QCD VetoQCD Veto
We estimate our expected background contribution to the sample in each lepton type and tag category. The following plots show our expected and observed number of background events as a function of jet multiplicity. Our search region is the two jet bin. The other jet bins are control regions.
2 SECVTX Tag Tight Lepton Events 2 SECVTX Tag Isolated Track Events
More Background Plots and Tables




Neural Network Input Variables

We use a Neural Network with 6 inputs, 11 hidden nodes, and one output node. The six input variables are
  • Dijet invariant mass+: The invariant mass reconstructed from the two jets. If there are additional looser jets, the loose jet that is closest to one of the two jets is included in this invariant mass calculation.
  • Total System $p_T$: The vector sum of the transverse momenta of the lepton, the MET, and the two jets.
  • PT Imbalance: The scalar sum of the lepton and jet transverse momenta minus the MET.
  • Sum ET (loose jets): The scalar sum of the loose jet transverse energy.
  • M min (lep + ν + jet): The invariant mass of the lepton, MET and one of the two jets, where the jet is chosen to give the minimum invariant mass. The PZ of neutrino is ignored for this quantity.
  • Δ R (lepton - ν): The distance between the direction of lepton and neutrino in η - φ plane, where the PZ of the neutrino is taken from largest PZ calculated from W mass constraint.
The following plots show the NN Inputs for pretag (control region) tight leptons.




More NN Input Plots

Other Kinematic Plots





Neural Network Output

The neural network output is a value between 0 and 1. Values close to 1 correspond to "more signal-like", values close to zero correspond to "less signal-like". We train a separate neural network for each Higgs signal mass. The following plots show the NN output in a few search channels.
Tight Leptons, One SECVTX Tag Tight Leptons, Two SECVTX Tags Tight Leptons, One SECVTX Tag, One JETPROB Tag
More NN Output Plots





Systematics:

We address systematic uncertainty on the signal acceptance from several different sources:
  • jet energy scale (JES)
  • initial and final state radiation (ISR/FSR)
  • parton distribution functions (PDF)
  • luminosity (lumi)
  • b-tagging scale factor
The following table shows the systematic uncertainty due to each effect on the signal acceptance.
b-tagging category Trigger Lepton (Isotrk) Reco Trigger ISR/FSR JES PDF b-tagging Total
One tag < 1% (8.9%) ~2-3% 2.9% 2.3% 1.2% 3.5% 5.7%
Two Tag < 1% (8.9%) ~2-3% 5.2% 2.5% 2.1% 8.4% 14%




Results:

We perform a binned likelihood fit of the neural network distribution where we constrain the backgrounds to their estimated rates within uncertainties. For optimal sensitivity, we perform a separate simultaneous search in each tag channel and lepton category. We set a limit on WH production cross section times branching ratio as a function of Higgs mass. We express our limits as a ratio to the Standard Model Cross section.
CDF Preliminary 2.7 fb-1
Limits for Combined Tag and Lepton Channels
Rates relative to Standard Model Expectation
Mass Observe Expect
100 3.6 4.3
105 3.6 4.6
110 3.7 5.0
115 5.2 5.8
120 5.6 6.9
125 8.2 8.2
130 8.9 10.0
135 12.4 13.8
140 23.1 19.4
145 30.6 28.9
150 61.1 43.2


Last modified: Mon Nov 17 18:05:21 CST 2008