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

Understand

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.