Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 063015 |
| Fachzeitschrift | Physical Review D |
| Jahrgang | 102 |
| Ausgabenummer | 6 |
| Publikationsstatus | Veröffentlicht - 14 Sept. 2020 |
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.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Physik und Astronomie (sonstige)
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in: Physical Review D, Jahrgang 102, Nr. 6, 063015, 14.09.2020.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Detection of gravitational-wave signals from binary neutron star mergers using machine learning
AU - Schäfer, Marlin B.
AU - Ohme, Frank
AU - Nitz, Alexander H.
N1 - Funding Information: We thank Tobias Blenke, Christoph Dreißigacker, and Tobias Florin for their comments and suggestions. We acknowledge the Max Planck Gesellschaft and the Atlas cluster computing team at Albert-Einstein Institut (AEI) Hannover for support. F. O. was supported by the Max Planck Society’s Independent Research Group Program.
PY - 2020/9/14
Y1 - 2020/9/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092350007&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2006.01509
DO - 10.48550/arXiv.2006.01509
M3 - Article
AN - SCOPUS:85092350007
VL - 102
JO - Physical Review D
JF - Physical Review D
SN - 2470-0010
IS - 6
M1 - 063015
ER -