  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.
