In the present study, I table the first detailed estimation of the magnetic noise contribution to the Advanced Virgo sensitivity to gravitational waves. I tackle the topic by performing experimental assessments and numerical finite element simulations, all accompanied by careful data analysis. Results suggest that the magnetic noise impact for Advanced Virgo is not dramatic, but it will eventually be a considerable issue once the detector will approach its final design. In anticipation of that, I propose a mitigation strategy based on passive magnetic field shielding. In the second part, I deal with seismic newtonian noise, focusing on two crucial aspects involving the noise cancellation pipeline. These are the choice of the subtraction filter and the optimization of the seismic sensor array placement. The former issue required the definition of a machine learning algorithm based on deep neural networks, and its fine tuning. Results give some indication of good performances compared to the standard Wiener filter approach. The problem of the sensors deployment is instead addressed with the finite element analysis of the actual Virgo infrastructure and underground soil layers surrounding the test masses.

Magnetic and Newtonian noises in Advanced Virgo: evaluation and mitigation strategies

CIRONE, ALESSIO
2020

Abstract

In the present study, I table the first detailed estimation of the magnetic noise contribution to the Advanced Virgo sensitivity to gravitational waves. I tackle the topic by performing experimental assessments and numerical finite element simulations, all accompanied by careful data analysis. Results suggest that the magnetic noise impact for Advanced Virgo is not dramatic, but it will eventually be a considerable issue once the detector will approach its final design. In anticipation of that, I propose a mitigation strategy based on passive magnetic field shielding. In the second part, I deal with seismic newtonian noise, focusing on two crucial aspects involving the noise cancellation pipeline. These are the choice of the subtraction filter and the optimization of the seismic sensor array placement. The former issue required the definition of a machine learning algorithm based on deep neural networks, and its fine tuning. Results give some indication of good performances compared to the standard Wiener filter approach. The problem of the sensors deployment is instead addressed with the finite element analysis of the actual Virgo infrastructure and underground soil layers surrounding the test masses.
23-mar-2020
Inglese
CHINCARINI, ANDREA
CANEPA, MAURIZIO
FERRANDO, RICCARDO
Università degli studi di Genova
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/68983
Il codice NBN di questa tesi è URN:NBN:IT:UNIGE-68983