Optimal measurement strategies for effective suppression of drift errors

Abstract

Drifting of experimental setups with change in temperature or other environmental conditions is the limiting factor of many, if not all, precision measurements. The measurement error due to a drift is, in some sense, in-between random noise and systematic error. In the general case, the error contribution of a drift cannot be averaged out using a number of measurements identically carried out over a reasonable time. In contrast to systematic errors, drifts are usually not stable enough for a precise calibration. Here a rather general method for effective suppression of the spurious effects caused by slow drifts in a large variety of instruments and experimental setups is described. An analytical derivation of an identity, describing the optimal measurement strategies suitable for suppressing the contribution of a slow drift described with a certain order polynomial function, is presented. A recursion rule as well as a general mathematical proof of the identity is given. The effectiveness of the discussed method is illustrated with an application of the derived optimal scanning strategies to precise surface slope measurements with a surface profiler.

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