A robust machine learning algorithm to search for continuous gravitational waves

Abstract

Many continuous gravitational wave searches are affected by instrumental spectral lines that could be confused with a continuous astrophysical signal. Several techniques have been developed to limit the effect of these lines by penalising signals that appear in only a single detector. We have developed a general method, using a convolutional neural network, to reduce the impact of instrumental artefacts on searches that use the SOAP algorithm. The method can identify features in corresponding frequency bands of each detector and classify these bands as containing a signal, an instrumental line, or noise. We tested the method against four different data-sets: Gaussian noise with time gaps, data from the final run of Initial LIGO (S6) with signals added, the reference S6 mock data challenge data set and signals injected into data from the second advanced LIGO observing run (O2). Using the S6 mock data challenge data set and at a 1% false alarm probability we showed that at 95% efficiency a fully-automated SOAP search has a sensitivity corresponding to a coherent signal-to-noise ratio of 110, equivalent to a sensitivity depth of 10 Hz${}^{-1/2}$, making this automated search competitive with other searches requiring significantly more computing resources and human intervention.

References in corpus (24)

• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• scikit-image: Image processing in Python
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
• Characterization of the LIGO detectors during their sixth science run
• LIGO: The Laser Interferometer Gravitational-Wave Observatory
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Matching matched filtering with deep networks in gravitational-wave astronomy
• All-sky search for continuous gravitational waves from isolated neutron stars using Advanced LIGO O2 data
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of Advanced LIGO
• Recent searches for continuous gravitational waves
• Continuous gravitational waves from neutron stars: current status and prospects
• Deep-Learning continuous gravitational waves
• Postprocessing methods used in the search for continuous gravitational-wave signals from the Galactic Center
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• Search for continuous gravitational waves: Improving robustness versus instrumental artifacts
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
• A comparison of methods for the detection of gravitational waves from unknown neutron stars
• Results of the deepest all-sky survey for continuous gravitational waves on LIGO S6 data running on the Einstein@Home volunteer distributed computing project
• SOAP: A generalised application of the Viterbi algorithm to searches for continuous gravitational-wave signals
• A new veto for continuous gravitational wave searches

Cited by in corpus (24)

• Searches for Continuous-Wave Gravitational Radiation
• All-sky search for continuous gravitational waves from isolated neutron stars using Advanced LIGO and Advanced Virgo O3 data
• Searches for continuous gravitational waves from neutron stars: A twenty-year retrospective
• Search methods for continuous gravitational-wave signals from unknown sources in the advanced-detector era
• Status and perspectives of Continuous Gravitational Wave searches
• Detecting Gravitational-waves from Extreme Mass Ratio Inspirals using Convolutional Neural Networks
• Generalized Approach to Matched Filtering using Neural Networks
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Rapid parameter estimation for an all-sky continuous gravitational wave search using conditional varitational auto-encoders
• Generalised gravitational burst generation with Generative Adversarial Networks
• Convolutional neural network search for long-duration transient gravitational waves from glitching pulsars
• Applying the Viterbi Algorithm to Planetary-Mass Black Hole Searches
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Assessing the impact of non-Gaussian noise on convolutional neural networks that search for continuous gravitational waves
• Novel neural-network architecture for continuous gravitational waves
• Gravitational-wave searches in the era of Advanced LIGO and Virgo
• Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks
• Large-kernel Convolutional Neural Networks for Wide Parameter-Space Searches of Continuous Gravitational Waves
• Assessing the similarity of continuous gravitational-wave signals to narrow instrumental artifacts
• Transformer Networks for Continuous Gravitational-wave Searches
• Unsupervised Domain Adaptation for Constraining Star Formation Histories
• Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks
• Gravitational waves from sub-solar mass primordial black holes
• Learning to detect continuous gravitational waves: an open data-analysis competition