Neural network interpolation of the magnetic field for the LISA Pathfinder Diagnostics Subsystem |
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| Authors: | Marc Diaz-Aguilo Alberto Lobo Enrique García–Berro |
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| Institution: | 1.Departament de Física Aplicada,Universitat Politècnica de Catalunya,Castelldefels,Spain;2.Institut d’Estudis Espacials de Catalunya,Barcelona,Spain;3.Institut de Ciències de l’Espai, CSIC, Facultat de Ciències,Bellaterra,Spain |
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| Abstract: | LISA Pathfinder is a science and technology demonstrator of the European Space Agency within the framework of its LISA mission,
which aims to be the first space-borne gravitational wave observatory. The payload of LISA Pathfinder is the so-called LISA
Technology Package, which is designed to measure relative accelerations between two test masses in nominal free fall. Its
disturbances are monitored and dealt by the diagnostics subsystem. This subsystem consists of several modules, and one of
these is the magnetic diagnostics system, which includes a set of four tri-axial fluxgate magnetometers, intended to measure
with high precision the magnetic field at the positions of the test masses. However, since the magnetometers are located far
from the positions of the test masses, the magnetic field at their positions must be interpolated. It has been recently shown
that because there are not enough magnetic channels, classical interpolation methods fail to derive reliable measurements
at the positions of the test masses, while neural network interpolation can provide the required measurements at the desired
accuracy. In this paper we expand these studies and we assess the reliability and robustness of the neural network interpolation
scheme for variations of the locations and possible offsets of the magnetometers, as well as for changes in environmental
conditions. We find that neural networks are robust enough to derive accurate measurements of the magnetic field at the positions
of the test masses in most circumstances. |
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