Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational‐wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational‐wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time–frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised‐learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational‐Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised‐learning architecture of the previous study is examined and reported. Transient noise appearing in the data from gravitational‐wave detectors frequently causes problems such as instability of the detectors and overlapping or mimicking gravitational‐wave signals. An architecture for classifying transient noise, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering, is proposed. In this study, the training process of the architecture is examined and reported.