Call-for-papers: “Machine Learning in Gravitational-Wave Science”
Published in Physics, Computational Sciences, and Statistics
The journals General Relativity and Gravitation and Living Reviews in Relativity have opened a joint Topical Collection on “Machine Learning in Gravitational-Wave Science”, which aims to include review articles and original research.
While the recent Living Review “Applications of machine learning in gravitational-wave research with current interferometric detectors” by Cuoco et al. (2025) serves as a general introduction to the topic, we encourage authors to submit their work on specific applications of machine learning methods, neural networks, and deep learning techniques in GW data analysis, including noise reduction and mitigation, signal detection, parameter estimation, classification and interpretation of astrophysical sources to the journal General Relativity and Gravitation.
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General Relativity and Gravitation
General Relativity and Gravitation is a journal devoted to all theoretical and experimental aspects of modern gravitational physics.
Related Collections
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Science with Space-Based Gravitational-Wave Detectors
This collection has evolved from the earlier series on "LISA: Science with the Laser Interferometer Space Antenna" and now aims to include White Papers, Invited Reviews, and Original Research on general space-based GW science from multiple research groups.
Publishing Model: Hybrid
Deadline: Ongoing
Machine Learning in Gravitational-Wave Science
A collection of articles on applications of machine learning techniques in gravitational-wave research.
While the Living Review "Applications of machine learning in gravitational-wave research with current interferometric detectors" (27 February 2025) serves as a general introduction to the topic, we encourage authors to submit their work on specific applications of machine learning methods, neural networks, and deep learning techniques in GW data analysis, including noise reduction and mitigation, signal detection, parameter estimation, classification and interpretation of astrophysical sources to the journal General Relativity and Gravitation.
Publishing Model: Hybrid
Deadline: Aug 01, 2026
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