Fast frequency reconstruction using Deep Learning for event recognition in ring laser data
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909823614844928 |
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| author | Di Somma, Giuseppe Carelli, Giorgio Di Virgilio, Angela D. V. Fuso, Francesco Maccioni, Enrico Marsili, Paolo |
| author_facet | Di Somma, Giuseppe Carelli, Giorgio Di Virgilio, Angela D. V. Fuso, Francesco Maccioni, Enrico Marsili, Paolo |
| contents | The reconstruction of a frequency with minimal delay from a sinusoidal signal is a common task in several fields; for example Ring Laser Gyroscopes, since their output signal is a beat frequency. While conventional methods require several seconds of data, we present a neural network approach capable of reconstructing frequencies of several hundred Hertz within approximately 10 milliseconds. This enables rapid trigger generation. The method outperforms standard Fourier-based techniques, improving frequency estimation precision by a factor of 2 in the operational range of GINGERINO, our Ring Laser Gyroscope.\\ In addition to fast frequency estimation, we introduce an automated classification framework to identify physical disturbances in the signal, such as laser instabilities and seismic events, achieving accuracy rates between 99\% and 100\% on independent test datasets for the seismic class. These results mark a step forward in integrating artificial intelligence into signal analysis for geophysical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast frequency reconstruction using Deep Learning for event recognition in ring laser data Di Somma, Giuseppe Carelli, Giorgio Di Virgilio, Angela D. V. Fuso, Francesco Maccioni, Enrico Marsili, Paolo Machine Learning Computational Physics Data Analysis, Statistics and Probability Geophysics The reconstruction of a frequency with minimal delay from a sinusoidal signal is a common task in several fields; for example Ring Laser Gyroscopes, since their output signal is a beat frequency. While conventional methods require several seconds of data, we present a neural network approach capable of reconstructing frequencies of several hundred Hertz within approximately 10 milliseconds. This enables rapid trigger generation. The method outperforms standard Fourier-based techniques, improving frequency estimation precision by a factor of 2 in the operational range of GINGERINO, our Ring Laser Gyroscope.\\ In addition to fast frequency estimation, we introduce an automated classification framework to identify physical disturbances in the signal, such as laser instabilities and seismic events, achieving accuracy rates between 99\% and 100\% on independent test datasets for the seismic class. These results mark a step forward in integrating artificial intelligence into signal analysis for geophysical applications. |
| title | Fast frequency reconstruction using Deep Learning for event recognition in ring laser data |
| topic | Machine Learning Computational Physics Data Analysis, Statistics and Probability Geophysics |
| url | https://arxiv.org/abs/2510.03325 |