Early warning of coalescing neutron-star and neutron-star-black-hole binaries from nonstationary noise background using neural networks

Abstract

The success of the multi-messenger astronomy relies on gravitational-wave observatories like LIGO and Virgo to provide prompt warning of merger events involving neutron stars (including both binary neutron stars and neutron-star-black-holes), which further depends critically on the low-frequency sensitivity of LIGO as a typical binary neutron star stays in this band for minutes. However, the current sub-60 Hz sensitivity of LIGO has not yet reached its design target and the excess noise can be more than an order of magnitude below 20 Hz. It is limited by nonlinearly coupled noises from auxiliary control loops which are also nonstationary, posing challenges to realistic early-warning pipelines. Nevertheless, machine-learning-based neural networks provide ways to simultaneously improve the low-frequency sensitivity and mitigate its nonstationarity, and detect the real-time gravitational-wave signal with a very short computational time. We propose to achieve this by inputting both the main gravitational-wave readout and key auxiliary witnesses to a compound neural network. Using simulated data with characteristic representing the real LIGO detectors, our machine-learning-based neural networks can reduce nonlinearly coupled noise by about a factor of 5 and allows a typical binary neutron star (neutron-star-black-hole) to be detected 100 s (10 s) before the merger at a distance of 40 Mpc (160 Mpc). If one can further reduce the noise to the fundamental limit, our neural networks can achieve detection out to a distance of 80 Mpc and 240 Mpc for binary neutron stars and neutron-star-black-holes, respectively. It thus demonstrates that utilizing machine-learning-based neural networks is a promising direction for the timely detection of the coalescence of electromagnetically bright LIGO/Virgo sources.

15 pages, 12 figures; to be submitted to PRD

References in corpus (17)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Multi-messenger Observations of a Binary Neutron Star Merger
• Advanced LIGO
• An Ordinary Short Gamma-Ray Burst with Extraordinary Implications: Fermi-GBM Detection of GRB 170817A
• GW170817: Joint Constraint on the Neutron Star Equation of State from Multimessenger Observations
• Sensitivity and Performance of the Advanced LIGO Detectors in the Third Observing Run
• A Highly Spinning and Aligned Binary Black Hole Merger in the Advanced LIGO First Observing Run
• Machine-learning non-stationary noise out of gravitational wave detectors
• Low-Latency Gravitational Wave Alerts for Multi-Messenger Astronomy During the Second Advanced LIGO and Virgo Observing Run
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
• First demonstration of early warning gravitational wave alerts
• Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
• Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
• Sub-threshold binary neutron star search in Advanced LIGO's first observing run
• The SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences

Cited by in corpus (25)

• Listening to the Universe with Next Generation Ground-Based Gravitational-Wave Detectors
• Uncertainty limits on neutron star radius measurements with gravitational waves
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Electromagnetic precursors to black hole - neutron star gravitational wave events: Flares and reconnection-powered fast-radio transients from the late inspiral
• Electromagnetic precursor flares from the late inspiral of neutron star binaries
• Reconnection-powered fast radio transients from coalescing neutron star binaries
• Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
• Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors
• Convolutional neural network for gravitational-wave early alert: Going down in frequency
• Realistic observing scenarios for the next decade of early warning detection of binary neutron stars
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
• Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
• Early warning of precessing neutron-star black-hole binary mergers with the near-future gravitational-wave detectors
• Searching for gravitational waves from stellar-mass binary black holes early inspiral
• Improving the stability of frequency dependent squeezing with bichromatic control of filter cavity length, alignment and incident beam pointing
• Exposing Gravitational Waves below the Quantum Shot Noise
• AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
• Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks
• Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers
• Exploring fundamental laws of classical mechanics via predicting the orbits of planets based on neural networks
• Learning and Interpreting Gravitational-Wave Features from CNNs with a Random Forest Approach
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets