21 FIG. 39: (Color online) The measured XENON10 background event rate (black) compared to Monte Carlo simulations (red) in the 5.4-kg fiducial mass. The energy is based on com- bined energy scale. The contribution from different compo- nents from the main detector and shield are shown in different colors. FTs represent feedthroughs. Poly represents polyethy- lene in the shield. IC and OC represent internal cryostat and outer cryostat separately. FIG. 40: (Color online) Event distribution in the ΔLog$_{10}$(S2/S1) vs. Energy space for the 58.4-day WIMP search data. The crosses indicate the events that “leak” into the nuclear recoil acceptance region, which is within the mean and 3 σ of the nuclear recoil band. Red circles indicate the re- maining events after all software cuts, discussed in the text. ee keV energy  1 10 2 10 3 10 rate [events/keV/kg/day] -5 10$^{-4}$ 10$^{-3}$ 10$^{-2}$ 10$^{-1}$ 10 1 10 MC WS4 data FTs PMTs Poly Teflon IC and OC Kr 85 Energy [keVee] (based on 2.2 phe/keVee) 0 2 4 6 8 10 12 14 16 18 20 (S2/S1) 10 Log Δ 1 1.5 2 2.5 3 3.5 NR, Mean σ NR, -3  = 300 pe threshold S2 Radius [mm] 0 10 20 30 40 50 60 70 80 90 100 s] Drift Time [- -80 -70 -60 -50 -40 -30 -20 -10 0 region around and below the bottom PMT array, as was shown in Fig. 23. For illustration, Fig 40 shows the ΔLog$_{10}$(S2/S1) vs. energy event distribution of the blinded WIMP search data. Prior to a final set of cuts, designed to remove these anomalous events, a total of 22 events is observed in the WIMP acceptance region. Only some of these events are expected from the statistical leakage of the electron recoil band. The spatial distribution of all events in the energy region of interest is shown in Fig 41. Two cuts, based on the S1 hit pattern, were identified to remove these anomalous events. One cut is based on the asymmetry of S1 between the top and bottom array of PMTs, i.e. p$_{asy}$ = ( S 1$_{t}$ − S 1$_{b}$) / ( S 1$_{t}$ + S 1$_{b}$). p$_{asy}$ is larger for anomalous events with some scatters in the region close to the top PMTs, but not in the sensitive LXe target. This cut also removes most of the events with misidentified S1 in software, e.g. a small S2 signal, or a micro-discharge pulse in the gas. Figure 42 shows the distribution of this parameter versus energy. A second set of cuts is based on the S1 signal distri- bution on the top and bottom PMT arrays. We define a parameter p$_{RMS}$ = √ 1 n $^{∑}$( S 1$_{i}$ − S 1) 2 from the Root- Mean-Square (RMS) value of the 5 (for top) or 10 (for bottom) PMTs receiving the largest hits. Anomalous events tend to give non-uniform S1-signal distributions, e.g., if one of the interactions occurs near the bottom PMT array. Figure 43 shows the effect of this cut for the bottom PMTs. The above software cuts also remove a small fraction of genuine nuclear recoil events. By comparing the AmBe nuclear recoil data before and after the software cuts, the estimated cut efficiency ϵ$_{cut}$ was calculated and shown in Table II. The particles potentially contributing to the back- ground of the XENON10 detector are alphas, betas, gam- mas, and neutrons. Neutrons scattering with Xe nuclei yield nuclear recoils, possibly mimicking a WIMP sig- Appendix A: The XENON10 Backgrounds FIG. 41: (Color online) Event distribution in the 58.4-day WIMP search data. The crosses indicate the events that “leak” into the nuclear recoil acceptance region, as shown in Fig 40 in the energy window (2-12 keVee) of interest. Red circles indicate the remaining events after all software cuts, discussed in the text.