Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular,...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Khan, Asad, Huerta, E A, Das, Arnav
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 25.08.2020
Subjects:	Algorithms Black holes Coalescing Computation Deep learning Machine learning Manifolds Mathematical models Neural networks Optimization Parameters Physics Waveforms
ISSN:	2331-8422
Online Access:	Get full text
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