Curvature error in overlap-reference transfer is the residual instrumental correction left when a curved operating-point response is represented by endpoint linear interpolation. Let be the actual log-ratio offset at optical power , and let interpolate its exact values at . Applying as the correction leaves
This deterministic model discrepancy is separate from random uncertainty in the measured endpoints.
The polynomial interpolation error formula gives, for twice continuously differentiable and between the endpoints,
for some in that interval. To derive it, subtract a multiple of chosen to make the error vanish also at ; two applications of Rolle's theorem make the second derivative vanish at an intermediate point. If , then
The maximum product occurs at the midpoint. Halving the reference spacing therefore quarters this worst-case bound when the same curvature bound remains justified.
For an original example, use power in milliwatts, endpoints , , and . The midpoint error is bounded by in log-ratio. The exact function
attains while matching both endpoint corrections perfectly. The corresponding multiplicative error in the corrected optical ratio is . Repeating endpoint measurements indefinitely would reduce their random uncertainty without revealing this midpoint discrepancy.
To guarantee throughout a reference interval under the stated bound, choose spacing satisfying
The numerical bound and tolerance require spacing no greater than . Outside the endpoint interval, the product error formula needs a curvature bound over the larger range including the extrapolation point; the interior maximum bound cannot simply be reused.
A proposed check measures additional intermediate power settings and repeats at the intended detector bias and beat frequency. It also monitors mode alignment and polarization, which the primary NIST heterodyne apparatus controls separately.[ref] Finitely many extra points constrain a response model but do not prove an arbitrary global curvature bound. State how is justified. The GUM's treatment of uncertainty propagation distinguishes local approximations from significant nonlinear effects.[ref]

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