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تسجيل مستخدم جديدRandom telegraph signal analysis with a recurrent neural network
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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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