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Communication Dans Un Congrès Année : 2022

Fast and accurate gravitational-wave modelling with principal component regression

Résumé

Inference from gravitational-wave observations relies on the availability of accurate theoretical waveform models to compare with the data. This paper considers the rapid generation of surrogate time-domain waveforms consistent with the gravitational-wave signature of the merger of spin-aligned binary black holes. Building on previous works, a machinelearning model is proposed that allows for highly-accurate waveform regression from a set of examples. An improvement of about an order of magnitude in accuracy with respect to the state of the art is demonstrated, along with a significant speed up in computing time with respect to the reference generation software tools.
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Dates et versions

hal-03868278 , version 1 (02-12-2022)

Identifiants

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Cyril Cano, Eric Chassande-Mottin, Nicolas Le Bihan. Fast and accurate gravitational-wave modelling with principal component regression. EUSIPCO 2022 - 30th European Signal Processing Conference, Aug 2022, Belgrade, Serbia. ⟨10.23919/EUSIPCO55093.2022.9909752⟩. ⟨hal-03868278⟩
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