Method of measurements with random perturbation: Application in photoemission experiments

TL;DR

This paper demonstrates that the SPSA algorithm effectively filters systematic noise with non-zero mean in photoemission spectra, improving data accuracy.

Contribution

It introduces the application of SPSA for noise filtering in photoemission experiments, showing its effectiveness in real data analysis.

Findings

SPSA accurately filters systematic noise in photoemission data.

The filtered spectrum closely matches noise-free measurements.

The method shows promise for broader experimental data evaluation.

Abstract

We report an application of a simultaneous perturbation stochastic approximation (SPSA) algorithm to filtering systematic noise (SN) with non-zero mean value in photoemission data. In our analysis we have used a series of 50 single-scan photoemission spectra of W(110) surface where randomly chosen SN was added. It was found that the SPSA-evaluated spectrum is in good agreement with the spectrum measured without SN. On the basis of our results a wide application of SPSA for evaluation of experimental data is anticipated.