Simulations of real-time system identification for superconducting cavities with a recursive least-squares algorithm

TL;DR

This paper evaluates a recursive least-squares algorithm for real-time identification of superconducting cavity parameters, demonstrating its effectiveness through simulations based on ESS cavity data and analyzing noise impacts.

Contribution

It introduces a recursive least-squares method for real-time superconducting cavity parameter estimation and assesses its performance with noise considerations.

Findings

The algorithm accurately estimates bandwidth and detuning in simulations.

Signal-to-noise ratio expressions help evaluate algorithm applicability.

Results suggest potential for real-time cavity diagnostics.

Abstract

We explore the performance of a recursive least-squares algorithm to determine the bandwidth $\omega_{12}$ and the detuning $\Delta\omega$ of a superconducting cavity. We base the simulations on parameters of the ESS double-spoke cavities. Expressions for the signal-to-noise ratio of derived parameters are given to explore the applicability of the algorithm to other configurations.