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arXiv · 2002.05817

Random telegraph signal analysis with a recurrent neural network

N. J. Lambert·A. A. Esmail·M. Edwards·A. J. Ferguson·H. G. L. Schwefel
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Abstract

We use an artificial neural network to analyze asymmetric noisy random telegraph signals (RTSs), and extract underlying transition rates. We demonstrate that a long short-term memory neural network can vastly outperform conventional methods, particularly for noisy signals. Our technique gives reliable results as the signal-to-noise ratio approaches one, and over a wide range of underlying transition rates. We apply our method to random telegraph signals generated by a superconducting double dot based photon detector, allowing us to extend our measurement of quasiparticle dynamics to new temperature regimes.

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N. J. Lambert, A. A. Esmail, M. Edwards, A. J. Ferguson, H. G. L. Schwefel. 2020-02-14. Random telegraph signal analysis with a recurrent neural network. https://doi.org/10.1103/physreve.102.012312

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