论文标题
随机电报信号分析与复发性神经网络
Random telegraph signal analysis with a recurrent neural network
论文作者
论文摘要
我们使用人工神经网络来分析不对称的嘈杂随机电报信号（RTSS），并提取潜在的过渡速率。我们证明，长期的短期记忆神经网络可以极大地超过常规方法，尤其是对于嘈杂的信号。随着信噪比接近1，并且超过了广泛的基础过渡速率，我们的技术可提供可靠的结果。我们将方法应用于由基于双点的光子检测器产生的随机电报信号，从而使我们可以将准粒子动力学的测量扩展到新的温度状态。
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.
