Matched filtering for gravitational wave detection without template bank driven by deep learning template prediction model bank

Abstract

The existing matched filtering method for gravitational wave (GW) search relies on a template bank. The computational efficiency of this method scales with the size of the templates within the bank. Higher-order modes and eccentricity will play an important role when third-generation detectors operate in the future. In this case, traditional GW search methods will hit computational limits. To speed up the computational efficiency of GW search, we propose the utilization of a deep learning (DL) model bank as a substitute for the template bank. This model bank predicts the latent templates embedded in the strain data. Combining an envelope extraction network and an astrophysical origin discrimination network, we realize a novel GW search framework. The framework can predict the GW signal's matched filtering signal-to-noise ratio (SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR holds greater physical interpretability than the $p_{score}$ metric. Moreover, the intermediate results generated by our approach, including the predicted template, offer valuable assistance in subsequent GW data processing tasks such as parameter estimation and source localization. Compared to the traditional matched filtering method, the proposed method can realize real-time analysis. The minor improvements in the future, the proposed method may expand to other scopes of GW search, such as GW emitted by the supernova explosion.

arXiv comment20 pages,12 figures

References in corpus (37)

• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run
• A gravitational-wave standard siren measurement of the Hubble constant
• Science Case for the Einstein Telescope
• The PyCBC search for gravitational waves from compact binary coalescence
• Tests of General Relativity with GW170817
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals
• Real-time gravitational-wave science with neural posterior estimation
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Detecting binary compact-object mergers with gravitational waves: Understanding and Improving the sensitivity of the PyCBC search
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Constraints on cosmic strings using data from the third Advanced LIGO-Virgo observing run
• The MBTA Pipeline for Detecting Compact Binary Coalescences in the Third LIGO-Virgo Observing Run
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• All-sky search for continuous gravitational waves from isolated neutron stars using Advanced LIGO and Advanced Virgo O3 data
• Science with the TianQin Observatory: Preliminary Results on Stochastic Gravitational-Wave Background
• Deep-Learning continuous gravitational waves
• Gravitational wave signal recognition of O1 data by deep learning
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Deep learning for multimessenger core-collapse supernova detection
• Detection and characterization of instrumental transients in LISA Pathfinder and their projection to LISA
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• Search for a scalar induced stochastic gravitational wave background in the third LIGO-Virgo observing run
• Training Strategies for Deep Learning Gravitational-Wave Searches
• Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
• Normalizing flows as an avenue to study overlapping gravitational wave signals
• Using Deep Learning to Localize Gravitational Wave Sources
• Improved deep learning techniques in gravitational-wave data analysis
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search
• Ensemble of Deep Convolutional Neural Networks for real-time gravitational wave signal recognition
• Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates
• Localization of gravitational waves using machine learning
• Implementation of the rROF denoising method in the cWB pipeline for gravitational-wave data analysis

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• Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
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