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Found 7 papers, 5 papers with code
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems.
Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
The detection of Extreme Mass Ratio Inspirals (EMRIs) is intricate due to their complex waveforms, extended duration, and low signal-to-noise ratio (SNR), making them more challenging to be identified compared to compact binary coalescences.
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes.
Space-based gravitational wave signal detection and extraction with deep neural network
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection.
Improved deep learning techniques in gravitational-wave data analysis
We point out that CNN models are robust to the variation of the parameter range of the GW waveform.
Gravitational wave signal recognition of O1 data by deep learning
Our adjusted convolutional neural network admits comparable accuracy and efficiency of signal recognition as other deep learning works published in the literature.
Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology
Reduced-order surrogate models for scalar-tensor gravity in the strong field and applications to binary pulsars and GW170817
We investigate the scalar-tensor gravity of Damour and Esposito-Far\`ese (DEF), which predicts non-trivial phenomena in the nonperturbative strong-field regime for neutron stars (NSs).
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena High Energy Physics - Phenomenology
