caa a22 4500 445830069 CHVBK 20180317145312.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s10686-011-9215-8 doi (NATIONALLICENCE)springer-10.1007/s10686-011-9215-8 Neural network interpolation of the magnetic field for the LISA Pathfinder Diagnostics Subsystem [Elektronische Daten] [Marc Diaz-Aguilo, Alberto Lobo, Enrique García-Berro] 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. Springer Science+Business Media B.V., 2011 LISAPathfinder nationallicence Magnetometers nationallicence On-board instrumentation nationallicence Spaceborne and space-research instruments nationallicence Neural networks nationallicence Diaz-Aguilo Marc Departament de Física Aplicada, Universitat Politècnica de Catalunya, c/Esteve Terrades 5, 08860, Castelldefels, Spain aut Lobo Alberto Institut d'Estudis Espacials de Catalunya, c/Gran Capità 2-4, Edif. Nexus 104, 08034, Barcelona, Spain aut García-Berro Enrique Departament de Física Aplicada, Universitat Politècnica de Catalunya, c/Esteve Terrades 5, 08860, Castelldefels, Spain aut Experimental Astronomy Springer Netherlands 30/1(2011-05-01), 1-21 0922-6435 30:1<1 2011 30 10686 https://doi.org/10.1007/s10686-011-9215-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10686-011-9215-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Diaz-Aguilo Marc Departament de Física Aplicada, Universitat Politècnica de Catalunya, c/Esteve Terrades 5, 08860, Castelldefels, Spain aut NATIONALLICENCE 700 1- Lobo Alberto Institut d'Estudis Espacials de Catalunya, c/Gran Capità 2-4, Edif. Nexus 104, 08034, Barcelona, Spain aut NATIONALLICENCE 700 1- García-Berro Enrique Departament de Física Aplicada, Universitat Politècnica de Catalunya, c/Esteve Terrades 5, 08860, Castelldefels, Spain aut NATIONALLICENCE 773 0- Experimental Astronomy Springer Netherlands 30/1(2011-05-01), 1-21 0922-6435 30:1<1 2011 30 10686 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer