A1 Journal article (refereed)
Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data Using Differential Evolution (2020)

Helenius, A. P., Puurtinen, T. A., Kinnunen, K. M., & Maasilta, I. J. (2020). Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data Using Differential Evolution. Journal of Low Temperature Physics, 200(5-6), 213-219. https://doi.org/10.1007/s10909-020-02489-0

JYU authors or editors

Publication details

All authors or editors: Helenius, A. P.; Puurtinen, T. A.; Kinnunen, K. M.; Maasilta, I. J.

Journal or series: Journal of Low Temperature Physics

ISSN: 0022-2291

eISSN: 1573-7357

Publication year: 2020

Volume: 200

Issue number: 5-6

Pages range: 213-219

Publisher: Springer Science and Business Media LLC

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1007/s10909-020-02489-0

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71073

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1909.05643


We discuss a robust method to simultaneously fit a complex multi-body model both to the complex impedance and the noise data for transition-edge sensors. 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 Levenberg–Marquardt (LM) algorithm. Test fits are made using both DE and LM 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 10 from the known values were used. It is shown that the least squares fitting becomes unreliable beyond a 10% deviation from the known values.

Keywords: research equipment; sensors; signal processing; differential evolution; genetic algorithms

Free keywords: thermal model; genetic algorithm; differential evolution; transition-edge sensor

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1

Last updated on 2021-24-11 at 11:03