Utilizing Convolutional Autoencoder for Anomaly Detections in LIGO Spectrogram Data

Abstract

Massive amounts of data generated from the continuous running of LIGO gravitational-wave detectors comes with a need to search for signals within the data. Gravitational-wave data captured by the detector consist of astronomical events or glitches that last seconds. We present an unsupervised learning using convolutional autoencoder trained on the no-glitch Gravity Spy dataset to do anomaly search on spectrogram data. Reconstruction error is used as the basis and multiple windows are used to improve the model. Results on test data show that the model is capable of detecting signals with significant anomalies such as the Chirp or Koi Fish glitch. Meanwhile, detecting subtle anomalies such as the 1400 Hz Ripples is difficult because its reconstruction error is near the range of noise signals. Validating the result on confirmed gravitational-wave signals shows that the model is capable of gravitational-wave detection.