Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1643220
- Resource Type:
- Published Article
- Journal Name:
- Physics Letters B
- Additional Journal Information:
- Journal Name: Physics Letters B Journal Volume: 808 Journal Issue: C; Journal ID: ISSN 0370-2693
- Publisher:
- Elsevier
- Country of Publication:
- Netherlands
- Language:
- English
Citation Formats
Khan, Asad, Huerta, E. A., and Das, Arnav. Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. Netherlands: N. p., 2020.
Web. doi:10.1016/j.physletb.2020.135628.
Khan, Asad, Huerta, E. A., & Das, Arnav. Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers. Netherlands. https://doi.org/10.1016/j.physletb.2020.135628
Khan, Asad, Huerta, E. A., and Das, Arnav. Tue .
"Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers". Netherlands. https://doi.org/10.1016/j.physletb.2020.135628.
@article{osti_1643220,
title = {Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers},
author = {Khan, Asad and Huerta, E. A. and Das, Arnav},
abstractNote = {},
doi = {10.1016/j.physletb.2020.135628},
journal = {Physics Letters B},
number = C,
volume = 808,
place = {Netherlands},
year = {Tue Sep 01 00:00:00 EDT 2020},
month = {Tue Sep 01 00:00:00 EDT 2020}
}
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https://doi.org/10.1016/j.physletb.2020.135628
https://doi.org/10.1016/j.physletb.2020.135628
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