Resolution-limit conditioning in finite-input detector calibration
ID: resolution-limit-conditioning-in-finite-input-detector-calibration
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 approaches the known input width . Subtracting two well-measured squared widths can therefore be a poorly conditioned inference.
For an original example, and give and . The two relative sensitivity coefficients are about and . 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 , imposing the physical constraint gives a detector-width interval , approximately . 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]
Calibration Service for Low-level Pulsed-Laser Radiometers at 1.06 µm: Pulse Energy and Peak Power by Rodney W. Leonhardt. Appendix C.1–C.2, printed pp. 36–37; approximately 120 ps optical input and Figure C1
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. New to topics? Read the docs here!