reruns, etc.).

![A demonstration of the upper limit validation technique for a sample band (101 Hz; October data). The x-axis is $h_{nom}$, $h_0$ divided by the for this upper-limit band; the eight points represent detection efficiencies for eight sets of 125 software injections. These eight points are then fit to a sigmoid curve (in black); the 95% upper limit can then be read off from the point where the curve crosses 95% detection efficiency.[]{data-label="ExamplePlot"}](SigmoidLeastSquareFit_101_0.pdf){width="49.00000%"}

A small number of $0.1$-Hz bands had outliers so large that the semi-analytic method failed to converge to an estimate for $h_{0}$. Instrumental artifacts at these frequencies were identified using run-averaged spectra and the respective 1-Hz bands were then rerun with the disturbed $0.1$-Hz subband excluded. The excluded bands are detailed in Table  \[ExclBand\].

Results
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![image](FullSpectrum95ULCorrectLivetimeMethodPSDSkyPosCorrectedAgeLimitP2.pdf){width="98.00000%"}

![Upper limits at 95% confidence (red circles) compared to the initial age-based limit on fiducial ellipticity $\epsilon $ (black line).[]{data-label="Ellip"}](FullSpectrum95ULCorrectLivetimeMethodPSDSkyPosCorrectedAgeLimitP2Ellipticity.pdf){width="49.00000%"}

![Upper limits at 95% confidence (red circles) compared to the initial age-based limit on $r$-mode amplitude $\alpha $ (black line).[]{data-label="RMode"}](FullSpectrum95ULCorrectLivetimeMethodPSDSkyPosCorrectedAgeLimitP2RModes.pdf){width="49.00000%"}

Figure  \[ULE\] shows the 95% confidence upper limits (ULs) over the full band for the July-August data set, which was the more sensitive of the two because of its 