Simultaneous Noise and Impedance Fitting to Transition-Edge Sensor Data Using Differential Evolution
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
Published inJournal of Low Temperature Physics
© The Authors 2020
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.
PublisherSpringer Science and Business Media LLC
Publication in research information system
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Additional information about fundingOpen access funding provided by University of Jyväskylä (JYU).
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