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

HIGHLIGHTS

• who: Yusuke Sakai et al. from the Research Center forCity University Tokyo, ‑0082, Japan have published the research: Unsupervised learning architecture for classifying the transient noise of interferometric gravitational‑wave detectors, in the Journal: Scientific Reports Scientific Reports
• what: Although the accuracy of the model is less than the that of above models, unsupervised learning has the advantage that | 12:9935 | 7 Vol.:(0123456789) data annotations are not required, and the model has the potential to suggest the existence of subclasses, as shown in "Evaluation of the architecture " section. In this study, the features of . . .
  

   

  

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