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

Listening to the Universe with Next Generation Ground-Based Gravitational-Wave Detectors

“…Radio observations could shed light on the magnetosphere interactions between the two NSs before merger and test the hypothesis that some fast radio bursts result in the aftermath of a BNS merger. As we shall argue below, it should be possible to send out alerts before the epoch of coalescence [142,[176][177][178][179][180][181][182][183] and efforts are underway to accomplish this during the fourth observing run of the LIGO and Virgo detectors [175,184,185].…”

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

“…As described in recent reviews (Huerta and Zhao, 2020 ; Cuoco et al, 2021 ), AI and high performance computing (HPC) as well as edge computing have been showcased to enable gravitational wave detection with the same sensitivity than template-matching algorithms, but orders of magnitude faster and at a fraction of the computational cost. At a glance, recent AI applications for gravitational wave astrophysics includes classification or signal detection (Gabbard et al, 2018 ; George and Huerta, 2018a , b ; Dreissigacker et al, 2019 ; Fan et al, 2019 ; Miller et al, 2019 ; Rebei et al, 2019 ; Beheshtipour and Papa, 2020 ; Deighan et al, 2020 ; Dreissigacker and Prix, 2020 ; Krastev, 2020 ; Li et al, 2020a ; Schäfer et al, 2020 , 2021 ; Skliris et al, 2020 ; Wang et al, 2020 ; Gunny et al, 2021 ; Lin and Wu, 2021 ; Schäfer and Nitz, 2021 ), signal denoising and data cleaning (Shen et al, 2019 ; Ormiston et al, 2020 ; Wei and Huerta, 2020 ; Yu and Adhikari, 2021 ), regression or parameter estimation (Gabbard et al, 2019 ; Chua and Vallisneri, 2020 ; Green and Gair, 2020 ; Green et al, 2020 ; Dax et al, 2021a , b ; Shen et al, 2022 ) Khan and Huerta 1 , accelerated waveform production (Chua et al, 2019 ; Khan and Green, 2021 ), signal forecasting (Lee et al, 2021 ; Khan et al, 2022 ), and early warning systems for gravitational wave sources that include matter, such as binary neutron stars or black hole-neutron star systems (Wei and Huerta, 2021 ; Wei et al, 2021a ; Yu et al, 2021 ).…”

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

“…If we can remove the excess contamination in the sub-60 Hz band, it would greatly promote a wide array of science cases including the early warning of binary neutron star mergers (Cannon et al, 2012 ; Abbott et al, 2019b ; Chu et al, 2020 ; Sachdev et al, 2020 ; Yu et al, 2021 ), the detection of intermediate-mass black holes (Mandel et al, 2008 ; Graff et al, 2015 ; Veitch et al, 2015 ; LIGO Scientific Collaboration and Virgo Collaboration, 2020 ), the constraining of eccentricities of binary black holes and hence their formation channels (Abbott et al, 2019 ; Romero-Shaw et al, 2019 ), and many more. Refer also the discussions in, e.g., Yu et al ( 2018 ) and references therein.…”

“…Fortunately, machine learning (ML) techniques, especially the use of convolutional neural networks (CNNs; Lecun et al, 2015 ), offer an attractive potential solution to the nonlinear noise regression problem (refer to, e.g., Ormiston et al, 2020 ; Vajente et al, 2020 ; Mogushi et al, 2021 ; Yu et al, 2021 ). By inputting to a CNN sufficiently many auxiliary witnesses that contain all the information about the noise coupling, and utilizing properly designed network structure and training strategies, we can let the algorithm figure out the coupling mechanism behind the noise even it involves nonlinearity and complicated blending of different sensors.…”

“…Furthermore, after the training process, the subsequent predicting of the contamination using CNNs is highly efficient computationally, allowing the noise cleaning to be performed in real time (i.e., online). This would be especially beneficial for searches that require low latency, such as the early warning of binary neutron star mergers (Baltus et al, 2021 ; Yu et al, 2021 ). Other successful usage of ML techniques in GW astronomy include the identification of various GW events (Bayley et al, 2020 ; Chan et al, 2020 ; Dreissigacker and Prix, 2020 ; Huerta et al, 2020 ; Krastev, 2020 ; Schäfer et al, 2020 ; Wong et al, 2020 ; Beheshtipour and Papa, 2021 ; Chang et al, 2021 ; Chatterjee et al, 2021 ; López et al, 2021 ; Marianer et al, 2021 ; Mishra et al, 2021 ; Saiz-Pérez et al, 2021 ; Wei and Huerta, 2021 ; Yan et al, 2021 ), source parameter estimations (Gabbard et al, 2019 ; Chatterjee et al, 2020 ; Chua and Vallisneri, 2020 ; Green et al, 2020 ; Talbot and Thrane, 2020 ; Álvares et al, 2021 ; D'Emilio et al, 2021 ; Krastev et al, 2021 ; Williams et al, 2021 ; Xia et al, 2021 ), and detector characterization (Biswas et al, 2020 ; Colgan et al, 2020 ; Cuoco et al, 2020 ; Essick et al, 2020 ; Torres-Forné et al, 2020 ; Mogushi, 2021 ; Sankarapandian and Kulis, 2021 ; Soni et al, 2021 ; Zhan et al, 2021 ).…”