Abstract
The sensitivity of gravitational waves (GWs) detectors is impacted by several sources of noise. One such source is related to the deposition of charge on the test masses (TMs), since its interaction with the surrounding electrical fields introduces an unwanted non-gravitational force on the TM. The charging process is still not completely understood and the charge deposition has historically never been consistently monitored, therefore we propose to implement a real-time monitoring system to overcome these limitations. In particular, in our work we explored 32 candidate sensor locations. Using simulation, we considered more than 100,000 different Gaussian-distributed charge located on either the front or back face of the TM. Utilizing dimensional reduction theories, like the PCA, we established sensor selection criteria to individuate the most relevant sensors. Exploiting Neural Networks (NNs), trained on the simulations, we determined the charge distributions.
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