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A coverage-preserving detector-width bound from a variance interval transforms uncertainty in a nonnegative response variance without imposing a symmetric width error bar. Let $D={\sigma }_h^2\ge 0$ and let a random interval $I(X)$ have stated frequentist coverage for $D$. Define the width confidence set byFor the true width ${\sigma }_h\ge 0$,The events are identical, so their coverage probabilities are identical. The transformation does not create coverage that the original interval lacked. Resolution-limit conditioning in finite-input detector calibration explains why working with the variance difference first is useful near a barely resolved detector response.

For an original explicit measurement model, suppose $\hat{D}$ has a normal distribution with mean $D$ and known standard deviation $s=0.025\mathrm{n}{\mathrm{s}}^2$. A reading $\hat{D}=0.040\mathrm{n}{\mathrm{s}}^2$ gives an approximately 95 percent interval $\hat{D}\pm 1.96s=[-0.009,0.089]\mathrm{n}{\mathrm{s}}^2$. Intersecting with the physical range and taking square roots gives $J=[0,0.2983]\mathrm{ns}$. This is an assumed normal model for measuring $D$, not a claim that any difference of fitted squared widths is automatically normal.

If the entire variance interval is negative, the result is an empty physical confidence set for a detector variance difference. Do not replace its negative upper endpoint by zero and report an apparently exact zero width: that changes the confidence-set construction. An empty realization signals disagreement between that interval and the physical parameter range; it can occur even in a valid procedure on an occasional sample that misses the true value.

NIST distinguishes a standard uncertainty from an interval with a justified confidence level and explains the distributional assumptions behind a coverage factor near two.^[ref] The normal interval above needs its stated model; the monotone transformation proof itself does not. Application to a detector additionally requires the nonnegative-profile convolution assumptions of finite-input correction in pulse-detector calibration; a correct statistical transformation cannot repair an invalid physical variance identity.

Known-input calibration of a pulse detector uses a characterized optical waveform to separate the incident signal from the receiver response. In a linear, baseline-corrected model,where $P_{\mathrm{ref}}$ is known input power, $h$ a normalized impulse response and $g$ the overall responsivity. Recording the complete response givesThus a known reference energy calibrates the area gain without requiring the input to be an ideal impulse. Shape calibration additionally uses the reference waveform.

For nonnegative normalized profiles with finite second moments, define $p=P_{\mathrm{ref}}/U_{\mathrm{ref}}$ and $q=y/\int y$. Since $q=p\ast h$, their temporal variances satisfy ${\sigma }_q^2={\sigma }_p^2+{\sigma }_h^2$. In an original chosen example, an input standard deviation $0.60\mathrm{ns}$ and recorded deviation $1.00\mathrm{ns}$ imply ${\sigma }_h=0.80\mathrm{ns}$. Treating that finite input as a delta impulse would incorrectly assign the full $1.00\mathrm{ns}$ to the detector. This finite-input correction in pulse-detector calibration concerns standard deviations; a universal subtraction rule for arbitrary FWHM values does not follow. Negative ringing also invalidates the nonnegative-profile interpretation used for the variance identity.

NIST's primary procedure illuminated the detector with a pulse much shorter than the detector response and characterized detector, cable and oscilloscope together at the relevant gain and signal levels.^[ref] Such a known input breaks the optical tail and detector-response ambiguity described by the parent article.

In the frequency domain, the same model is $Y=g{P}_{\mathrm{ref}}H$. Division can estimate $H$ where the input spectrum is known and sufficiently nonzero; small input components amplify measurement noise. A short reference pulse is useful because it excites a broad frequency range, but detector linearity, baseline, area coverage and reference uncertainty still need checking.

Resolution-limit conditioning in finite-input detector calibration is the sensitivity of a small inferred detector width to uncertainty in two nearly equal measured widths. In finite-input correction in pulse-detector calibration, the nonnegative normalized convolution model givesThe signed relative sensitivities areThey diverge as the measured output width ${\sigma }_q$ approaches the known input width ${\sigma }_p$. Subtracting two well-measured squared widths can therefore be a poorly conditioned inference.

For an original example, ${\sigma }_q=1.00\mathrm{ns}$ and ${\sigma }_p=0.99\mathrm{ns}$ give $D=0.0199\mathrm{n}{\mathrm{s}}^2$ and ${\sigma }_h\approx 0.1411\mathrm{ns}$. The two relative sensitivity coefficients are about $50.25$ and $-49.25$. A small independent fractional width error can produce a much larger fractional response-width error; a common multiplicative timebase error instead partly cancels because the coefficients sum to one. Correlations must therefore be retained.^[ref]

Near the boundary, report uncertainty in the variance difference before transforming it. For a chosen interval $D\in [-0.010,0.050]\mathrm{n}{\mathrm{s}}^2$, imposing the physical constraint $D\ge 0$ gives a detector-width interval $[0,\sqrt{0.050}]\mathrm{ns}$, approximately $[0,0.2236]\mathrm{ns}$. A coverage-preserving detector-width bound from a variance interval uses this monotone square-root transformation on the physical intersection. It need not resemble a symmetric error bar around a point estimate. An interval wholly below zero contains no admissible width under this model; replacing it silently with a zero-width result hides the inconsistency.

NIST characterized its detector/cable/oscilloscope chain with an optical pulse much shorter than the receiver response.^[ref] That design avoids subtracting two nearly equal broad widths. The variance identity still requires nonnegative profiles and finite second moments; arbitrary full widths at half maximum and responses with negative ringing need a different model.