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LISA Pathfinder is a science and technology demonstrator of the EuropeanSpace Agency within the framework of its LISA mission, which aims to be thefirst space-borne gravitational wave observatory. The payload of LISAPathfinder is the so-called LISA Technology Package, which is designed tomeasure relative accelerations between two test masses in nominal free fall.Its disturbances are monitored and dealt by the diagnostics subsystem. Thissubsystem consists of several modules, and one of these is the magneticdiagnostics system, which includes a set of four tri-axial fluxgatemagnetometers, intended to measure with high precision the magnetic field atthe positions of the test masses. However, since the magnetometers are locatedfar from the positions of the test masses, the magnetic field at theirpositions must be interpolated. It has been recently shown that because thereare not enough magnetic channels, classical interpolation methods fail toderive reliable measurements at the positions of the test masses, while neuralnetwork interpolation can provide the required measurements at the desiredaccuracy. In this paper we expand these studies and we assess the reliabilityand robustness of the neural network interpolation scheme for variations of thelocations and possible offsets of the magnetometers, as well as for changes inenvironmental conditions. We find that neural networks are robust enough toderive accurate measurements of the magnetic field at the positions of the testmasses in most circumstances.

Neural network interpolation of the magnetic field for the LISA Pathfinder Diagnostics Subsystem