ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-08302020-184201


Thesis type
Tesi di laurea magistrale
Author
BINI, SOPHIE
URN
etd-08302020-184201
Thesis title
Unsupervised classification of short transient noise to improve gravitational wave detection
Department
FISICA
Course of study
FISICA
Supervisors
relatore Prof. Fidecaro, Francesco
correlatore Prof. Razzano, Massimiliano
Keywords
  • unsupervised clustering
  • glitches in gravitational wave detectors
  • transient noise in gravitational wave detectors
  • gravitational wave detector characterization
  • convolutional autoencoder
Graduation session start date
16/09/2020
Availability
Full
Summary
Gravitational waves interferometers are complex and sensitive detectors, whose data are non stationary and non Gaussian. Short duration disturbances, called ’glitches’, are caused by the instrument itself or by its interactions with the environment. These transient noises are particularly concerning as they can mimic gravitational wave signals, and have both high rate and high signal-to-noise ratio. Great effort has been made in the latest years to understand their causes and mitigate them: in particular as glitches differ significantly in terms of duration and frequency, their classification is crucial to trace back their origin. Due to glitches complexity, huge number and time evolving nature, machine learning techniques find great application in this field. This thesis proposes an unsupervised clustering algorithm able to group transient noise into different classes according to their morphology in a time-frequency map, using a neural network called ’autoencoder’ and a density-based clustering algorithm, without any prior knowledge on the data it is applied to. This method is successfully tested on LIGO Hanford detector glitches, and applied to latest Virgo data, clustering one week, randomly selected, of Virgo transient noise and contributing to a candidate event validation. The information acquired could enhance detector characterization and transient noise mitigation, improving gravitational-wave searches
File