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Tesi etd-06192020-081853


Tipo di tesi
Tesi di dottorato di ricerca
Autore
DI RENZO, FRANCESCO
URN
etd-06192020-081853
Titolo
Characterisation and mitigation of non-stationary noise in Advance Gravitational Wave Detectors
Settore scientifico disciplinare
FIS/01
Corso di studi
FISICA
Relatori
tutor Dott. Cella, Giancarlo
tutor Prof. Fidecaro, Francesco
commissario Dott. Gennai, Alberto
commissario Prof. Pinto, Innocenzo
commissario Prof. Arnaud, Nicolas
Parole chiave
  • statistical signal processing
  • LIGO
  • machine learning
  • gravitational waves
  • image processing
  • detector characterisation
  • data analysis
  • Virgo
Data inizio appello
26/06/2020
Consultabilità
Completa
Riassunto
In December 2019, the LIGO Scientific Collaboration and the Virgo Collaboration have published the results of their first two joint observing runs (O1 and O2), describing the source properties of ten binary black hole (BBH) and one binary neutron star (BNS) events. Their validation has been made employing state of the art Data Analysis techniques. All of them rely, to some extent, on the assumption to know the statistical properties of the detector noise, from which the gravitational signals are extracted. Moreover, their performances are optimal, with respect to certain criteria, if the noise distribution is stationary and Gaussian. To this purpose, in this Thesis work we have studied several strategies aimed at the verification of the previous two hypotheses, and the characterisation of the detector noise. Once a specific noise feature, detrimental for gravitational wave searches, was found, we have proceeded to the investigation of its causes and some mitigation strategies. The techniques that we have implemented have been selected from many fields of research, like Digital Image Processing and state of the art Machine Learning. Two original contributions have been introduced. One consists of a new method for the identification of generic non-stationary noise, from the variations in the empirical distribution of the signal RMS value. The other is a wavelet-based, instantaneous causality statistic, specifically aimed at the study of transient noises. These aim to improve upon other existing strategies and have been applied for the investigation of specific noise issue in Advanced Virgo detector data.
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