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The success of the multi-messenger astronomy relies on gravitational-waveobservatories like LIGO and Virgo to provide prompt warning of merger eventsinvolving neutron stars (including both binary neutron stars andneutron-star-black-holes), which further depends critically on thelow-frequency sensitivity of LIGO as a typical binary neutron star stays inthis band for minutes. However, the current sub-60 Hz sensitivity of LIGO hasnot yet reached its design target and the excess noise can be more than anorder of magnitude below 20 Hz. It is limited by nonlinearly coupled noisesfrom auxiliary control loops which are also nonstationary, posing challenges torealistic early-warning pipelines. Nevertheless, machine-learning-based neuralnetworks provide ways to simultaneously improve the low-frequency sensitivityand mitigate its nonstationarity, and detect the real-time gravitational-wavesignal with a very short computational time. We propose to achieve this byinputting both the main gravitational-wave readout and key auxiliary witnessesto a compound neural network. Using simulated data with characteristicrepresenting the real LIGO detectors, our machine-learning-based neuralnetworks can reduce nonlinearly coupled noise by about a factor of 5 and allowsa 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 furtherreduce the noise to the fundamental limit, our neural networks can achievedetection out to a distance of 80 Mpc and 240 Mpc for binary neutron stars andneutron-star-black-holes, respectively. It thus demonstrates that utilizingmachine-learning-based neural networks is a promising direction for the timelydetection of the coalescence of electromagnetically bright LIGO/Virgo sources.

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