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dc.contributor.author
Barsotti, Damián
dc.contributor.author
Cerino, Franco
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Tiglio, Manuel
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Villanueva, Uziel Aarón
dc.date.available
2023-06-26T17:56:38Z
dc.date.issued
2022-03
dc.identifier.citation
Barsotti, Damián; Cerino, Franco; Tiglio, Manuel; Villanueva, Uziel Aarón; Gravitational wave surrogates through automated machine learning; IOP Publishing; Classical and Quantum Gravity; 39; 8; 3-2022; 1-16
dc.identifier.issn
0264-9381
dc.identifier.uri
http://hdl.handle.net/11336/201568
dc.description.abstract
We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual case-by-case analyses and fine-tuning. The particular study of this article is within the context of the gravitational waves emitted by the collision of two spinless black holes in initial quasi-circular orbit. We find, for example, that approaches such as Gaussian process regression with radial bases as kernels, an approach which is generalizable to multiple dimensions with low computational evaluation cost, do provide a sufficiently accurate solution. The results here presented suggest that AutoML might provide a framework for regression in the field of surrogates for gravitational waveforms. Our study is within the context of surrogates of numerical relativity simulations based on reduced basis and the empirical interpolation method, where we find that for the particular case analyzed AutoML can produce surrogates which are essentially indistinguishable from the NR simulations themselves.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IOP Publishing
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
MACHINE LEARNING
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REDUCED ORDER MODELING
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WAVEFORM SURROGATES
dc.subject.classification
Astronomía
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Ciencias Físicas
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CIENCIAS NATURALES Y EXACTAS
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Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
Gravitational wave surrogates through automated machine learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-06-26T13:48:27Z
dc.journal.volume
39
dc.journal.number
8
dc.journal.pagination
1-16
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Barsotti, Damián. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
dc.description.fil
Fil: Cerino, Franco. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.description.fil
Fil: Tiglio, Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
dc.description.fil
Fil: Villanueva, Uziel Aarón. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
dc.journal.title
Classical and Quantum Gravity
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://iopscience.iop.org/article/10.1088/1361-6382/ac5ba1
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1088/1361-6382/ac5ba1
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