A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals
Created by W.Langdon from
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- @Article{Barone:2023:MLST,
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author = "Francesco Pio Barone and Daniele Dell'Aquila and
Marco Russo",
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title = "A novel multi-layer modular approach for real-time
fuzzy-identification of gravitational-wave signals",
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journal = "Machine Learning: Science and Technology",
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year = "2023",
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volume = "4",
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number = "4",
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pages = "045054",
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month = dec,
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keywords = "genetic algorithms, genetic programming, BP,
gravitational-wave science, analysis of noisy
timeseries, fuzzy-classification of signals,
speech-processing, artificial neural networks, ANN",
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publisher = "IOP Publishing",
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ISSN = "2632-2153",
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URL = "https://iopscience.iop.org/article/10.1088/2632-2153/ad1200",
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DOI = "doi:10.1088/2632-2153/ad1200",
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size = "18 pages",
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abstract = "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 of low
signal-to-noise-ration GW signals, against 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.",
- }
Genetic Programming entries for
Francesco Pio Barone
Daniele Dell'Aquila
Marco Russo
Citations