Resumen de Magnetic diagnostics algorithms for LISA Pathfinder: system identification and data analysis
Marc Díaz Aguiló
LISA (Laser Interferometer Space Antenna) is a joint mission of ESA and NASA, which aims to be the first space-borne gravitational wave observatory. LISA will consist in a constellation of three spacecraft at the vertexes of an equilateral triangle of side 5 million kilometers. The constellation will orbit around the Sun trailing the Earth by some 20 degrees. Each of the spacecraft harbors two proof masses, carefully protected against external disturbances such as solar radiation pressure and charged particles, which ensures they are in nominal free-fall in the interplanetary gravitational field. Gravitational waves will show as differential accelerations between pairs of proof masses, and the main aim of LISA is to measure such acceleration using laser interferometry. The technologies required for the LISA mission are many and challenging. This, coupled with the fact that some flight hardware cannot be tested on ground, led ESA to define a technology demonstrator to test in flight the required critical technologies. This precursor mission is called LISA Pathfinder (LPF). The payload of LISA Pathfinder is the LISA Technology Package (LTP), and will be the highest sensitivity geodesic explorer flown to date. The LISA Technology Package is designed to measure relative accelerations between two test masses in nominal free fall placed in a single spacecraft, since one LISA arm is squeezed from 5 million kilometer to 35 cm. Its success will prove the maturity of the necessary technologies for LISA such as the Optical Metrology System and the Drag Free concept. The differential acceleration reading will be perturbed by identified disturbances, such as thermal fluctuations or magnetic effects. These disturbances are monitored by the Diagnostics Subsystem. The Magnetic Diagnostics System is one of its modules and is a critical subsystem, since magnetic noise is apportioned to 40% of the total noise budget. In this respect, to estimate the magnetic noise contribution, the Magnetic Diagnostics Subsystem will have two main tasks: (1) estimate the magnetic properties of the test masses, i.e., their remanent magnetic moment and susceptibility, and (2) infer the magnetic field and its gradient at the location of the test masses. To this end, the Magnetic Diagnostics Subsystem includes two coils which generate controlled magnetic fields at the locations of the test masses. These magnetic fields will excite the dynamical response of both test masses. Thus, by adequate processing of the kinematic excursions delivered by the interferometer, the magnetic characteristics of the test masses can be estimated within 1% accuracy level. Additionally, the Magnetic Diagnostic Subsystem includes a set of four tri-axial fluxgate magnetometers. However, the magnetic field and its gradient need to be measured at the positions of the test masses and the readouts of the magnetometers do not provide a direct measurement of the magnetic field at these positions. Thus, an interpolation method must be implemented to calculate them. This is a difficult problem, mostly because the magnetometers are too distant from the locations of the test masses (more than 20 cm away) and because there are not sufficient magnetic channels to go beyond a classical linear interpolation method, which yields extremely poor interpolation results. Consequently, in this thesis we present and validate an alternative interpolation method based on neural networks. We put forward its robustness and accuracy in several mission scenarios and we stress the importance of an extensive magnetic testing campaign. Under these assumptions, we deliver magnetic field and gradient estimates with 10% accuracy. Finally, the estimate of the magnetic noise contribution to the total acceleration between the two LPF’s test masses is determined with an accuracy of 15%. This result represents an enhancement of the estimation quality in one order of magnitude with respect to former studies.
