Neural network time-series classifiers for gravitational-wave searches in single-detector periods

Abstract

The search for gravitational-wave signals is limited by non-Gaussian transient noises that mimic astrophysical signals. Temporal coincidence between two or more detectors is used to mitigate contamination by these instrumental glitches. However, when a single detector is in operation, coincidence is impossible, and other strategies have to be used. We explore the possibility of using neural network classifiers and present the results obtained with three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. The classifiers are trained on a month of data from the LIGO Livingston detector during the first observing run (O1) to identify data segments that include the signature of a binary black hole merger. Their performances are assessed and compared. We then apply trained classifiers to the remaining three months of O1 data, focusing specifically on single-detector times. The most promising candidate from our search is 2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance of this event to the level conventionally followed in gravitational-wave searches, we show that the signal is compatible with the merger of two black holes with masses $m_1=50.7_{-8.9}^{+10.4}{M}_{\odot }$ and $m_2=24.4_{-9.3}^{+20.2}{M}_{\odot }$ at the luminosity distance of $d_L=564_{-338}^{+812}\mathrm{Mpc}$.

29 pages, 11 figures, submitted to CQG

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• Gravity Spy: Lessons Learned and a Path Forward
• New Gravitational Wave Discoveries Enabled by Machine Learning
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Search for exotic gravitational wave signals beyond general relativity using deep learning