dc.identifier.uri |
http://dx.doi.org/10.15488/10648 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/10726 |
|
dc.contributor.author |
Schäfer, Martin B.
|
|
dc.contributor.author |
Ohme, Frank
|
|
dc.contributor.author |
Nitz, Alexander H.
|
|
dc.date.accessioned |
2021-03-26T10:06:21Z |
|
dc.date.available |
2021-03-26T10:06:21Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Schäfer, M.B.; Ohme, F.; Nitz, A.H.: Detection of gravitational-wave signals from binary neutron star mergers using machine learning. In: Physical Review D 102 (2020), Nr. 6, 63015. DOI: https://doi.org/10.1103/PhysRevD.102.063015 |
|
dc.description.abstract |
As two neutron stars merge, they emit gravitational waves that can potentially be detected by Earth-bound detectors. Matched-filtering-based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network-based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from nonspinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of ten per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 4 in sensitivity to signals with a signal-to-noise ratio between 8 and 15. However, this approach is not yet competitive with traditional matched-filtering-based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can be applied not only to machine-learning-based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches. © 2020 authors. Published by the American Physical Society. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
College Park, MD : American Physical Society |
|
dc.relation.ispartofseries |
Physical Review D 102 (2020), Nr. 6 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Condensed matter |
eng |
dc.subject |
Nuclear physics |
eng |
dc.subject |
Particles (Nuclear physics) |
eng |
dc.subject |
Quantum gravity |
eng |
dc.subject |
General relativity (Physics) |
eng |
dc.subject |
Gravitation |
eng |
dc.subject |
Fluid dynamics |
eng |
dc.subject.ddc |
530 | Physik
|
ger |
dc.title |
Detection of gravitational-wave signals from binary neutron star mergers using machine learning |
|
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
1089-4918 |
|
dc.relation.essn |
1550-2368 |
|
dc.relation.essn |
2470-0029 |
|
dc.relation.issn |
2470-0010 |
|
dc.relation.doi |
https://doi.org/10.1103/PhysRevD.102.063015 |
|
dc.bibliographicCitation.issue |
6 |
|
dc.bibliographicCitation.volume |
102 |
|
dc.bibliographicCitation.firstPage |
63015 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|