The magnetic diagnostics subsystem of the LISA Technology Package

Abstract

The Magnetic Diagnostics Subsystem of the LISA Technology Package (LTP) on board the LISA Pathfinder (LPF) spacecraft includes a set of four tri-axial fluxgate magnetometers, intended to measure with high precision the magnetic field at the positions they occupy. However, their readouts do not provide a direct measurement of the magnetic field at the positions of the test masses. Therefore, an interpolation method must be implemented to obtain this information. However, such interpolation process faces serious difficulties. Indeed, the size of the interpolation region is excessive for a linear interpolation to be reliable, and the number of magnetometer channels does not provide sufficient data to go beyond that poor approximation. Recent research points to a possible alternative to address the magnetic interpolation problem by means of neural network algorithms. The key point of this approach is the ability neural networks have to learn from suitable training data representing the magnetic field behaviour. Despite the large distance to the test masses and the insufficient magnetic readings, artificial neural networks are able to significantly reduce the estimation error to acceptable levels. The learning efficiency can be best improved by making use of data obtained from on-ground measurements prior to mission launch in all relevant satellite locations and under real operation conditions. Reliable information on that appears to be essential for a meaningful assessment of magnetic noise in the LTP.