A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals

Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extreme...

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Veröffentlicht in:Machine learning: science and technology Jg. 4; H. 4; S. 45054 - 45071
Hauptverfasser: Barone, Francesco Pio, Dell’Aquila, Daniele, Russo, Marco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.12.2023
Schlagworte:
analysis of noisy timeseries
Artificial neural networks
Complexity
Data analysis
Exploitation
False alarms
fuzzy-classification of signals
Genetic algorithms
genetic programming
Gravitational waves
gravitational-wave science
Interferometers
Laser interferometry
Machine learning
Modularity
Multilayers
Neural networks
Real time
Speech processing
ISSN:2632-2153, 2632-2153
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Zusammenfassung:Advanced LIGO and Advanced Virgo ground-based interferometers are instruments capable to detect gravitational wave (GW) signals exploiting advanced laser interferometry techniques. The underlying data analysis task consists in identifying specific patterns in noisy timeseries, but it is made extremely complex by the incredibly small amplitude of the target signals. In this scenario, the development of effective GW detection algorithms is crucial. We propose a novel layered framework for real-time detection of GWs inspired by speech processing techniques and, in the present implementation, based on a state-of-the-art machine learning approach involving a hybridization of genetic programming and neural networks. The key aspects of the newly proposed framework are: the well structured, layered approach, and the low computational complexity. The paper describes the basic concepts of the framework and the derivation of the first three layers. Even if, in the present implementation, the layers are based on models derived using a machine learning approach, the proposed layered structure has a universal nature. Compared to more complex approaches, such as convolutional neural networks, which comprise a parameter set of several tens of MB and were tested exclusively for fixed length data samples, our framework has lower accuracy (e.g. it identifies 45 % of low signal-to-noise-ration GW signals, against 65 % of the state-of-the-art, at a false alarm probability of 10 −2 ), but has a much lower computational complexity (it exploits only 4 numerical features in the present implementation) and a higher degree of modularity. Furthermore, the exploitation of short-term features makes the results of the new framework virtually independent against time-position of GW signals, simplifying its future exploitation in real-time multi-layer pipelines for gravitational-wave detection with new generation interferometers.
Bibliographie:MLST-101444.R1
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ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ad1200