Space-Based Gravitational Wave Observatories

Gair, J. R.; Hewitson, M.; Mueller, G.

doi:10.1007/978-981-15-4702-7_3-1

Cited by 3 publications

(5 citation statements)

References 207 publications

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“…From an astrophysical perspective, first, LISA will observe MBHBs with very high SNR, typically bigger than 100, out to very high redshift 11 . Then for EMRI signals, due to its physical complexity, the detection SNR threshold is ~30 11 . Next, for the LISA verification binaries almost half of them reach a SNR≥30 48 .…”

Section: Resultsmentioning

confidence: 99%

“…Galaxies and MBHBs coevolved during the evolution of the Universe. So the observation of GWs from the MBHB system can improve our understanding of important astronomical phenomena such as the formation of the MBH and the merging of galaxies 11 .…”

Section: Methodsmentioning

confidence: 99%

“…The intrinsic parameter is the frequency of the signal f and its derivative _ f . Frequency evolves slowly and some binaries will be chirping to higher frequency due to the decay of the orbit through the emission of GWs, but other binaries will be moving to lower frequency as a result of mass transfer between the binary components driving an increase in the orbital separation 11 .…”

Section: Methodsmentioning

confidence: 99%

“…Laser Interferometer Space Antenna (LISA) will be launched around 2034 8 , and Taiji 9 and TianQin 10 are also in progress. LISA is expected to observe a variety of GW sources 11 , including Galactic binaries (GB), massive black hole binaries (MBHB), and extreme-mass-ratio-inspirals (EMRI). The most common GB sources are binary white dwarf (BWD) systems, which will populate the whole frequency band of the LISA detector.…”

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confidence: 99%

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Space-based gravitational wave signal detection and extraction with deep neural network

et al. 2023

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Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Space-based gravitational wave signal detection and extraction with deep neural network

et al. 2023

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“…Detecting such signals is straightforward since the noise is negligible. However, for other source types like EMRI and BWD, the SNR tends to hover around 30 [25,26]. To facilitate practical considerations, we focus on gravitational waves falling within the 30-50 SNR range as in [27].…”

Section: Introductionmentioning

confidence: 99%

Random Convolutional Kernels for Space-Detector Based Gravitational Wave Signals

Poghosyan,

Luo

2023

Electronics

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Neural network models have entered the realm of gravitational wave detection, proving their effectiveness in identifying synthetic gravitational waves. However, these models rely on learned parameters, which necessitates time-consuming computations and expensive hardware resources. To address this challenge, we propose a gravitational wave detection model tailored specifically for binary black hole mergers, inspired by the Random Convolutional Kernel Transform (ROCKET) family of models. We conduct a rigorous analysis by factoring in realistic signal-to-noise ratios in our datasets, demonstrating that conventional techniques lose predictive accuracy when applied to ground-based detector signals. In contrast, for space-based detectors with high signal-to-noise ratios, our method not only detects signals effectively but also enhances inference speed due to its streamlined complexity—a notable achievement. Compared to previous gravitational wave models, we observe a significant acceleration in training time while maintaining acceptable performance metrics for ground-based detector signals and achieving equal or even superior metrics for space-based detector signals. Our experiments on synthetic data yield impressive results, with the model achieving an AUC score of 96.1% and a perfect recall rate of 100% on a dataset with a 1:3 class imbalance for ground-based detectors. For high signal-to-noise ratio signals, we achieve flawless precision and recall of 100% without losing precision on datasets with low-class ratios. Additionally, our approach reduces inference time by a factor of 1.88.

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