Enhancing gravitational-wave science with machine learning

Cuoco, E.; Powell, J.; Cavaglià, M.; Ackley, K.; Bejger, M.; Chatterjee, C.; Coughlin, M.; Coughlin, S.; Easter, P.; Essick, R.; Gabbard, H.; Gebhard, T.; Ghosh, S.; Haegel, L.; Iess, A.; Keitel, D.; Márka, Z.; Márka, S.; Morawski, F.; Nguyen, T.; Ormiston, R.; Pürrer, M.; Razzano, M.; Staats, K.; Vajente, G.; Williams, D.

doi:10.1088/2632-2153/abb93a

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Enhancing gravitational-wave science with machine learning

Cuoco, E., Powell, J., Cavaglià, M., Ackley, K., Bejger, M., Chatterjee, C., et al. (2020). Enhancing gravitational-wave science with machine learning. Machine Learning: Science and Technology, 2(1): 011002. doi:10.1088/2632-2153/abb93a.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-263A-A Version Permalink: https://hdl.handle.net/21.11116/0000-000B-263B-9

Genre: Journal Article

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https://arxiv.org/abs/2005.03745 (Preprint) Open Access Green

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Green

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https://doi.org/10.1088/2632-2153/abb93a (Publisher version) Open Access Gold

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Creators:
Cuoco, E.¹, Author
Powell, J.¹, Author
Cavaglià, M.¹, Author
Ackley, K.¹, Author
Bejger, M.¹, Author
Chatterjee, C.¹, Author
Coughlin, M.¹, Author
Coughlin, S.¹, Author
Easter, P.¹, Author
Essick, R.¹, Author
Gabbard, H.¹, Author
Gebhard, T.², Author
Ghosh, S.¹, Author
Haegel, L.¹, Author
Iess, A.¹, Author
Keitel, D.¹, Author
Márka, Z.¹, Author
Márka, S.¹, Author
Morawski, F.¹, Author
Nguyen, T.¹, Author
Ormiston, R.¹, AuthorPürrer, M.¹, AuthorRazzano, M.¹, AuthorStaats, K.¹, AuthorVajente, G.¹, AuthorWilliams, D.¹, Author more..

Affiliations:
1External Organizations, ou_persistent22
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647

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Free keywords: Abt. Schölkopf

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Language(s): eng - English

Dates: Submitted: 2020-05-07Published Online: 2020-12-01

Publication Status: Published online

Pages: 19

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Table of Contents: -

Rev. Type: Peer

Identifiers: DOI: 10.1088/2632-2153/abb93a
BibTex Citekey: Cuocoetal20

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Title: Machine Learning: Science and Technology

Abbreviation : Mach. Learn.: Sci. Technol.

Source Genre: Journal

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Publ. Info: Bristol, UK : IOP Publishing

Pages: - Volume / Issue: 2 (1) Sequence Number: 011002 Start / End Page: - Identifier: ISSN: 2632-2153
CoNE: https://pure.mpg.de/cone/journals/resource/2632-2153