Adaptation of a Convolutional Neural Network-based Pipeline to Detect Short GravitationalWave Bursts

data analysis; gravitational waves; machine learning; Background noise; Convolutional neural network; Core collapse supernovae; Gravitational wave interferometers; Gravitational-waves; Machine-learning; NET architecture; Network-based; Spectrograms; Wave transients; Multidisciplinary

Abstract :

[en] We present a machine learning based pipeline to analyze unmodeled gravitational wave (GW) transients of less than 10 s. The convolutional neural network (CNN) is based on a U-NET architecture and takes as input data from GW interferometers represented as time-frequency maps, returning a spectrogram without the background noise. The CNN has been trained on simulated data, using a generated Gaussian background noise and injecting GW signals from core-collapse supernovae (CCSNe) simulations. The pipeline is able to successfully denoise spectrograms and recognize as signals also CCSNe waveforms for which it has not been trained on.

Disciplines :

Space science, astronomy & astrophysics

Author, co-author :

Pracchia, Matteo ; Université de Liège - ULiège > Unités de recherche interfacultaires > Space sciences, Technologies and Astrophysics Research (STAR)

Language :

English

Title :

Adaptation of a Convolutional Neural Network-based Pipeline to Detect Short GravitationalWave Bursts

Publication date :

December 2024

Event name :

41st Liège International Astrophysical Colloquium

Event place :

Liège, Belgium

Event date :

15-19 July 2024

By request :

Yes

Audience :

International

Journal title :

Bulletin de la Société Royale des Sciences de Liège

ISSN :

0037-9565

eISSN :

1783-5720

Publisher :

Societe Royale des Sciences de Liege

Volume :

Issue :

Pages :

374 - 378

Peer review/Selection committee :

Editorial Reviewed verified by ORBi

Funding text :

I would like to thank Prof. Maxime Fays for the idea of the project and Prof. Jean-Rene Cudell for the discussions and suggestions on the topic. This work was supported by the Fonds de la Recherche Scientifique-FNRS, Belgium, under grant No. 4.4501. The author is grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459.

Available on ORBi :

since 27 May 2025

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Bibliography

Jebur, R. S., Zabil, M. H. B. M., Hammood, D. A., and Cheng, L. K. (2024) A comprehensive review of image denoising in deep learning. Multimedia Tools and Applications, 83(20), 58 181-58 199. https://doi.org/10.1007/s11042-023-17468-2.
Boudart, V. and Fays, M. (2022) Machine learning algorithm for minute-long burst searches. PhRvD, 105(8), 083007. https://doi.org/10.1103/PhysRevD.105.083007.
Boudart, V. (2023) Detection of minute-long Gravitational Wave transients using Deep Learning methods. Ph.D. thesis, Universite de Liege [Sciences], Liege (BE). https://hdl. handle.net/2268/308320.
Nitz, A., Harry, I., Brown, D., Biwer, C. M., Willis, J., Canton, T. D., Capano, C., Dent, T., Pekowsky, L., Davies, G. S. C., De, S., Cabero, M., Wu, S., Williamson, A. R., Machenschalk, B., Macleod, D., Pannarale, F., Kumar, P., Reyes, S., dfinstad, Kumar, S., Tapai, M., Singer, L., Kumar, P., veronica-villa, maxtrevor, Gadre, B. U. V., Khan, S., Fairhurst, S., and Tolley, A. (2024). gwastro/pycbc: v2.3.3 release of pycbc. Zenodo [Software]. https://doi.org/10.5281/zenodo.10473621.
Xing, F., Xie, Y., Shi, X., Chen, P., Zhang, Z., and Yang, L. (2019) Towards pixel-topixel deep nucleus detection in microscopy images. BMC Bioinformatics, 20. https://doi.org/10.1186/s12859-019-3037-5.
Powell, J. and Muller, B. (2019) Gravitational wave emission from 3D explosion models of core-collapse supernovae with low and normal explosion energies. MNRAS, 487(1), 1178-1190. https://doi.org/10.1093/mnras/stz1304.
Radice, D., Morozova, V., Burrows, A., Vartanyan, D., and Nagakura, H. (2019) Characterizing the gravitational wave signal from core-collapse supernovae. ApJL, 876(1), L9. https://doi.org/10.3847/2041-8213/ab191a.
O'Connor, E. P. and Couch, S. M. (2018) Exploring fundamentally three-dimensional phenomena in high-fidelity simulations of core-collapse supernovae. ApJ, 865(2), 81. https://doi.org/10.3847/1538-4357/aadcf7.
Powell, J. and Muller, B. (2020) Three-dimensional core-collapse supernova simulations of massive and rotating progenitors. MNRAS, 494(4), 4665-4675. https://doi.org/10.1093/mnras/staa1048.
Boudart, V. (2023) Convolutional neural network to distinguish glitches from minute-long gravitational wave transients. PhRvD, 107(2), 024007. https://doi.org/10.1103/PhysRevD. 107.024007.