Fast frequency reconstruction using DL for event recognition in ring laser data (arxiv.org)

Researchers propose a deep‑learning method to reconstruct instantaneous frequency from sinusoidal signals—specifically the beat frequency output of ring laser gyroscopes—at millisecond latency. Their neural network reliably estimates frequencies of several hundred hertz in roughly 10 ms, a dramatic speedup compared with conventional Fourier‑based estimators that require seconds of data. In the operational range of the GINGERINO ring laser gyroscope the DL estimator also halves frequency estimation error (≈2× improvement in precision) versus standard spectral methods, enabling fast trigger generation for transient events. Beyond fast estimation, the team built an automated classification pipeline to flag physical disturbances (e.g., laser instabilities and seismic events) in the same data stream, reporting 99–100% accuracy on independent test sets for the seismic class. Combined, the low‑latency frequency reconstruction and high‑fidelity disturbance classification open the door to real‑time monitoring, prompt event detection, and potentially closed‑loop control or QA for precision gyroscopes and other interferometric sensors. The work demonstrates a practical, high‑impact use of AI in geophysical instrumentation where reduced latency and improved precision materially enhance detection and response capabilities.