Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data using Differential Evolution

TL;DR

This paper presents a differential evolution-based method for robustly fitting complex impedance and noise data of transition-edge sensors, outperforming traditional least squares fitting especially with large parameter deviations.

Contribution

The paper introduces a differential evolution algorithm for simultaneous noise and impedance fitting in TES, demonstrating improved robustness over least squares methods.

Findings

Differential evolution provides accurate fits with large parameter deviations.

LS fitting becomes unreliable beyond 10% deviation from known values.

DE fitting maintains robustness with parameter limits up to five times the known values.

Abstract

We discuss a robust method to simultaneously fit a complex model both to the complex impedance and the noise data for transition-edge sensors (TES). It is based on a differential evolution (DE) algorithm, providing accurate and repeatable results with only a small increase in computational cost compared to the standard least squares (LS) fitting method. Test fits are made using both DE and LS methods, and the results compared with previously determined best fits, with varying initial value deviations and limit ranges for the parameters. The robustness of DE is demonstrated with successful fits even when parameter limits up to a factor of 5 from the known values were used. It is shown that the least squares fitting becomes unreliable beyond a 10% deviation from the known values.