Triggers

The triggers of interest are

  1. HLT_Photon10_L1R
    • L1 Seed: hltL1sRelaxedSingleEgammaEt5
    • Last Filter: hltL1NonIsoHLTNonIsoSinglePhotonEt10HcalIsolFilter
  2. HLT_Photon15_L1R
    • L1 Seed: hltL1sRelaxedSingleEgammaEt8
    • Last Filter: hltL1NonIsoHLTNonIsoSinglePhotonEt15HcalIsoFilter
  3. HLT_Ele10_LW_L1R
    • L1 Seed: hltL1sRelaxedSingleEgammaEt5
    • Last Filter: hltL1NonIsoHLTNonIsoSingleElectronLWEt10PixelMatchFilter
  4. HLT_Ele15_LW_L1R
    • L1 Seed: hltL1sRelaxedSingleEgammaEt8
    • Last Filter: hltL1NonIsoHLTNonIsoSingleElectronLWEt15PixelMatchFilter

Samples

Monte Carlo samples used in the study of electron trigger efficiencies are listed in Table 1.

TABLE 1 Sample Location of PAT Files
ttbar /RelValTTbar/CMSSW_3_6_2-MC_36Y_V10-v1/GEN-SIM-RECO /store/relval/CMSSW_3_6_2/RelValTTbar/GEN-SIM-RECO/MC_36Y_V10-v1/

Trigger Object Matching

To determine the trigger efficiencies, each PAT electron in the MC sample should be matched to an L1 seed or trigger event. This is done in the following way:

  • L1 Seed matching criteria:
    • $\Delta\phi$ < 0.522
    • for $\eta$ < 1.4791, $\Delta\eta$ < 0.261. For $\eta$ > 1.4791, $\Delta\eta$ < 0.5

  • HLT matching criteria:
    • $\Delta R$ < 0.2

The L1 seed matching criteria are derived from Sam Harper's L1 seed matching code, which can be found here. A simple $\Delta R$ matching for the L1 seed runs into trouble in the endcap, where the x-y geometry means that the crystals are large in eta. A more sophisticated, but also more relaxed cut is required here.

ttbar Electron Cuts and Plots

The cuts used are:

  • $p_{T}$ > 1.0 GeV
  • $|\eta|$ < 2.4
  • eidRobustLoose

The $p_{T}$ plot overflow has been transferred to the last bin in the histogram.

All efficiency plots were generated by defining a TGraphAsymmErrors in ROOT, and calling the member function BayesDivide(). This function takes two histograms (one the numerator and the other the denominator) and computes the efficiencies, while assigning suitable asymmetric errors using Bayesian statistics. Information about the TGraphAsymmErrors class can be found here, and a paper explaining the computation of the errors (which goes beyond normal binomial distribution errors) can be found here.

In the root macro, fits for the $p_{T}$ can be done by setting the last argument in fitHistoAsymm to '1'. This performs a fit with the following function:

$y=\frac{A}{2}(1 + erf(\frac{x - x_{0}}{s\sqrt{x}}))$

In the plots below, p0 = A, p1 = $x_{0}$ and p2 = s. p0 corresponds to the high $p_{T}$ limit of the efficiency, $x_{0}$ the midpoint of the slope, and s the gradient of the slope at $x=x_{0}$.

All $p_{T}$ curves are plotted with respect to supercluster $E_{T}$, via

patCandidate->superCluster()->energy())/(cosh(eleTriggerMatches[iEle].patCandidate->superCluster()->eta()))

All eta plots are taken at values more than 15 GeV to avoid any turn on effects.

-- Main.HongwanLiu - 16-Jul-2010

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Topic revision: r2 - 2010-07-22 - HongwanLiu
 
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