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Signal Modeling and Efficiency Mass resolutionMass resolution

2 As discussed in the introduction, sufficiently strong

6.6 Signal Modeling and Efficiency Mass resolutionMass resolution

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Figure 6.12: The distribution of the classifier scores for data (markers) and signal MC simulations (solid lines) for dark photon lifetimes corresponding to (top)cτA0 = 0.1mm, (middle) cτA0 = 1mm, and (bottom) cτA0 = 10mm. The MC simulations are arbitrarily normalized.

Event selection results

For signal with prompt decays, a total of 69 events pass all the selection criteria. The corresponding(mΥD,mA0)distribution is shown in Fig. 6.14. Most events correspond toe+e → qq¯process. The events nearmΥD ∼0.1 GeV andmA0 ∼0.05 GeV arise frome+e →γγγevents in which all three photons convert toe+e pairs.

For signal with displaced decays, a total of 56, 33, and 31 events are selected for the cτA0 =0.1, 1, and 10 mm data sample, respectively. The resulting mass distributions are shown in Fig. 6.15.

6.6 Signal Modeling and Efficiency

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Figure 6.13: Fits to the classification score distributions of data with a function of the form p(x) = ea·x2+b·x+c, forC0(upper),C1(middle), andC2(bottom) category of events.

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Figure 6.14: The(mΥD,mA0)distribution for events passing all selection criteria for prompt dark photon decays.

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Figure 6.15: The(mΥD,mA0)mass distribution of event candidates passing all selec- tion criteria for the datasets optimized for each dark photon lifetime.

than a simple Gaussian fit. While the fits are not perfect, they are sufficient for the purpose of estimating the mass resolution.

To evaluate the mass resolution as a function of(mΥD,mA0), we interpolate the results at known points using a 2-dimensional smooth spline. Smooth spline function aims to fit a polynomial function on data and guarantees the smoothness of the function at the same time. To minimize the fitting uncertainties, we fit each category of events individually. The results are shown in Fig. 6.17. TheΥDmass resolutions are at the level of 20 MeV. The dark photon mass resolutions are at the level of 3 MeV.

Signal efficiency

The signal efficiency is evaluated by counting the fraction of simulated events passing the selection criteria in the signal window at a given point, normalized to the total number of generated events. The size of the signal window is given by the mass resolutions at that point, namely 4∆mΥ and 4∆mA0.

To evaluate the signal efficiency as a function of (mΥD,mA0), we also use a 2- dimensional smooth spline technique for interpolation, fitting each category sepa- rately. The degrees of the polynomial functions used for interpolation are increased until the model is stable. The results of the signal acceptance, selection efficiency, and signal efficiency are shown in Fig. 6.18.

The signal acceptance is low when mΥD and mA0 are low, which stops us from improving the signal efficiency in the low mass region. The MVA prefers to select events in the range of 6 GeV ≤ mΥD ≤ 9 GeV, thus the selection efficiencies are higher in this region.

For each signal window, the signal efficiencies fromC0,C1, andC2are weighted by branching fractions to obtain the total signal efficiency of the signal window. The total signal efficiency is then used for signal extraction. The result is shown in Fig.

6.19. The two horizontal bands aroundmA0 = 0.78 GeV and 1.0 GeV correspond to the ω and φ resonances. The drop in the branching fraction A0 → X+X,X = e, µ, πatmA0 =0.78 GeV and 1.0 GeV is due to theA0decaying predominantly into π+ππ0andK+K, respectively.

Lifetime dependency

The signal efficiencies depend on the dark photon lifetime. When the dark photons have non-zero lifetime, the χ2fit of assuming prompt decay becomes larger and the mass resolution worse. The larger the dark photon lifetime, the smaller the signal

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Figure 6.16: Example of CBF fits to theΥD andA0mass spectrum (mΥD =8.0 GeV andmA0 = 0.5 GeV).

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Figure 6.17: (Left) TheΥDand (right) the dark photon mass resolution as a function ofmΥD andmA0 for each category of events.

efficiency. We generate and reconstruct the signal MC with non-zero lifetime to estimate and interpolate the impact of lifetime on the signal efficiencies. We use the functional form log() = a ·log(l)+ bto fit the relation between the average signal efficiencies () and the dark photon lifetime (l). We checked that the results obtained for each mass hypothesis are compatible with those obtained by considering the average efficiency, but the later are more robust and were chosen to describe the global dependence on the lifetime. For a given mass hypothesis(mΥD,mA0)with prompt signal efficiency 0, the relation between the signal efficiency and the dark photon lifetime under the mass hypothesis is reasonably described by:

log()= a· (log(l) −log(l0))+log(0), (6.10)

Figure 6.18: The acceptance, selection efficiency, and signal efficiency as a function ofmΥD andmA0 for each category of events.

withl0= 0.1 mm, and0the efficiency for prompt decays. For a dark photon flight lengths above 100 µm, this interpolated relation will be used to correct the signal efficiency and establish the kinetic mixing strength upper limit. When the dark photon flight length is below 100 µm, we use the efficiency determined for prompt decays, as the displaced decay vertices of the dark photon have negligible effect on the signal efficiencies.