Unsupervised learning architecture for classifying the transient noise of interferometric gravitational-wave detectors.

ABSTRACT: In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time-frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corre

SUBMITTER: Sakai Y 

PROVIDER: S-EPMC9200730 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature