LISA Technology Package Research Articles

In-orbit performance of the LISA Pathfinder drag-free and attitude control system

LISA Pathfinder is a technology demonstrator mission that was funded by the European Space Agency and that was launched on December 3, 2015. LISA Pathfinder has been conducting experiments to demonstrate key technologies for the gravitational wave observatory LISA in its operational orbit at the L1 Lagrange point of the Earth–Sun system until final switch off on July 18, 2017. These key technologies include the inertial sensors, the optical metrology system, a set of µ-propulsion cold gas thrusters and in particular the high performance drag-free and attitude control system (DFACS) that controls the spacecraft in 15 degrees of freedom during its science phase. The main goal of the DFACS is to shield the two test masses inside the inertial sensors from all external disturbances to achieve a residual differential acceleration between the two test masses of less than 3 × 10−14 m/s2/√Hz over the frequency bandwidth of 1–30 mHz. This paper focuses on two important aspects of the DFACS that has been in use on LISA Pathfinder: the DFACS Accelerometer mode and the main DFACS Science mode. The Accelerometer mode is used to capture the test masses after release into free flight from the mechanical grabbing mechanism. The main DFACS Science Mode is used for the actual drag-free science operation. The DFACS control system has very strong interfaces with the LISA Technology Package payload which is a key aspect to master the design, development, and analysis of the DFACS. Linear as well as non-linear control methods are applied. The paper provides pre-flight predictions for the performance of both control modes and compares these predictions to the performance that is currently achieved in-orbit. Some results are also discussed for the mode transitions up to science mode, but the focus of the paper is on the Accelerometer mode performance and on the performance of the Science mode in steady state. Based on the achieved results, some lessons learnt are formulated to extend the results to the drag-free control system to be designed for future space-based gravity wave observatories like LISA.

Constraints on LISA Pathfinder’s self-gravity: design requirements, estimates and testing procedures

LISA Pathfinder satellite was launched on 3 December 2015 toward the Sun–Earth first Lagrangian point (L1) where the LISA Technology Package (LTP), which is the main science payload, will be tested. LTP achieves measurements of differential acceleration of free-falling test masses (TMs) with sensitivity below within the 1–30 mHz frequency band in one-dimension. The spacecraft itself is responsible for the dominant differential gravitational field acting on the two TMs. Such a force interaction could contribute a significant amount of noise and thus threaten the achievement of the targeted free-fall level. We prevented this by balancing the gravitational forces to the sub nm s−2 level, guided by a protocol based on measurements of the position and the mass of all parts that constitute the satellite, via finite element calculation tool estimates. In this paper, we will introduce the gravitational balance requirements and design, and then discuss our predictions for the balance that will be achieved in flight.

Bayesian statistics for the calibration of the LISA Pathfinder experiment

The main goal of LISA Pathfinder (LPF) mission is to estimate the acceleration noise models of the overall LISA Technology Package (LTP) experiment on-board. This will be of crucial importance for the future space-based Gravitational-Wave (GW) detectors, like eLISA. Here, we present the Bayesian analysis framework to process the planned system identification experiments designed for that purpose. In particular, we focus on the analysis strategies to predict the accuracy of the parameters that describe the system in all degrees of freedom. The data sets were generated during the latest operational simulations organised by the data analysis team and this work is part of the LTPDA Matlab toolbox. A post-publication change was made to this article on 26 Jun 2020 to add an author.

The LISA Pathfinder Mission

LISA Pathfinder (LPF), the second of the European Space Agency's Small Missions for Advanced Research in Technology (SMART), is a dedicated technology validation mission for future spaceborne gravitational wave detectors, such as the proposed eLISA mission. LISA Pathfinder, and its scientific payload - the LISA Technology Package - will test, in flight, the critical technologies required for low frequency gravitational wave detection: it will put two test masses in a near-perfect gravitational free-fall and control and measure their motion with unprecedented accuracy. This is achieved through technology comprising inertial sensors, high precision laser metrology, drag-free control and an ultra-precise micro-Newton propulsion system. LISA Pathfinder is due to be launched in mid-2015, with first results on the performance of the system being available 6 months thereafter.The paper introduces the LISA Pathfinder mission, followed by an explanation of the physical principles of measurement concept and associated hardware. We then provide a detailed discussion of the LISA Technology Package, including both the inertial sensor and interferometric readout. As we approach the launch of the LISA Pathfinder, the focus of the development is shifting towards the science operations and data analysis - this is described in the final section of the paper

In-flight thermal experiments for LISA Pathfinder: Simulating temperature noise at the Inertial Sensors

Thermal Diagnostics experiments to be carried out on board LISA Pathfinder (LPF) will yield a detailed characterisation of how temperature fluctuations affect the LTP (LISA Technology Package) instrument performance, a crucial information for future space based gravitational wave detectors as the proposed eLISA. Amongst them, the study of temperature gradient fluctuations around the test masses of the Inertial Sensors will provide as well information regarding the contribution of the Brownian noise, which is expected to limit the LTP sensitivity at frequencies close to 1 mHz during some LTP experiments. In this paper we report on how these kind of Thermal Diagnostics experiments were simulated in the last LPF Simulation Campaign (November, 2013) involving all the LPF Data Analysis team and using an end-to-end simulator of the whole spacecraft. Such simulation campaign was conducted under the framework of the preparation for LPF operations.

Thermo-elastic induced phase noise in the LISA Pathfinder spacecraft

During the on-station thermal test campaign of the LISA Pathfinder, the diagnostics subsystem was tested in nearly space conditions for the first time after integration in the satellite. The results showed the compliance of the temperature measurement system, obtaining temperature noise around in the frequency band 1–30 mHz. In addition, controlled injection of heat signals to the suspension struts anchoring the LISA Technology Package (LTP) core assembly to the satellite structure allowed us to experimentally estimate, for the first time, the phase noise contribution through thermo-elastic distortion of the LTP interferometer, the satelliteʼs main instrument. Such contribution was found to be at , a factor of 30 below the measured noise at the lower end of the measurement bandwidth ().

Space-based detectors

The parallel session C5 on Space-Based Detectors gave a broad overview over the planned space missions related to gravitational wave detection. Overviews of the revolutionary science to be expected from LISA was given by Alberto Sesana and Sasha Buchman. The launch of LISA Pathfinder (LPF) is planned for 2015. This mission and its payload “LISA Technology Package” will demonstrate key technologies for LISA. In this context, reference masses in free fall for LISA, and gravitational physics in general, was described by William Weber, laser interferometry at the pico-metre level and the optical bench of LPF was presented by Christian Killow and the performance of the LPF optical metrology system by Paul McNamara. While LPF will not yet be sensitive to gravitational waves, it may nevertheless be used to explore fundamental physics questions, which was discussed by Michele Armano. Some parts of the LISA technology that are not going to be demonstrated by LPF, but under intensive development at the moment, were presented by Oliver Jennrich and Oliver Gerberding. Looking into the future, Japan is studying the design of a mid-frequency detector called DECIGO, which was discussed by Tomotada Akutsu. Using atom interferometry for gravitational wave detection has also been recently proposed, and it was critically reviewed by Peter Bender. In the nearer future, the launch of GRACE Follow-On (for Earth gravity observation) is scheduled for 2017, and it will include a Laser Ranging Interferometer as technology demonstrator. This will be the first inter-spacecraft laser interferometer and has many aspects in common with the LISA long arm, as discussed by Andrew Sutton.

Bayesian model selection for LISA pathfinder

The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment onboard the LPF. These models are used for simulations, but, more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes factor between two competing models. In our analysis, we use three main different methods to estimate it: the reversible jump Markov chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments in which the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored.

Improving Markov Chain Monte Carlo algorithms in LISA Pathfinder Data Analysis

The LISA Pathfinder mission (LPF) aims to test key technologies for the future LISA mission. The LISA Technology Package (LTP) on-board LPF will consist of an exhaustive suite of experiments and its outcome will be crucial for the future detection of gravitational waves. In order to achieve maximum sensitivity, we need to have an understanding of every instrument on-board and parametrize the properties of the underlying noise models. The Data Analysis team has developed algorithms for parameter estimation of the system. A very promising one implemented for LISA Pathfinder data analysis is the Markov Chain Monte Carlo. A series of experiments are going to take place during flight operations and each experiment is going to provide us with essential information for the next in the sequence. Therefore, it is a priority to optimize and improve our tools available for data analysis during the mission. Using a Bayesian framework analysis allows us to apply prior knowledge for each experiment, which means that we can efficiently use our prior estimates for the parameters, making the method more accurate and significantly faster. This, together with other algorithm improvements, will lead us to our main goal, which is no other than creating a robust and reliable tool for parameter estimation during the LPF mission.

LISA PathFinder radiation monitor proton irradiation test results

The design of the Radiation Monitor in the LISA Technology Package on board LISA Pathnder is based on two silicon PIN diodes, placed parallel to each other in a telescopic configuration. One of them is able to record spectral information of the particle hitting the diode. A test campaign for the flight model Radiation Monitor was done in the Paul Scherrer Institute Proton Irradiation Facility in September 2010. Its purpose was to check correct functionality of the Radiation Monitor under real high energy proton fluxes. Here we present the results of the experiments done and their assessment by means of a simulated flight model geometry using GEANT4 toolkit. No deviation from nominal RM performance was detected, which means the instrument is fully ready for flight.

State-space modelling for heater induced thermal effects on LISA Pathfinder's Test Masses

The OSE (Offline Simulations Environment) simulator of the LPF (LISA Pathfinder) mission is intended to simulate the different experiments to be carried out in flight. Amongst these, the thermal diagnostics experiments are intended to relate thermal disturbances and interferometer readouts, thereby allowing the subtraction of thermally induced interferences from the interferometer channels. In this paper we report on the modelling of these simulated experiments, including the parametrisation of different thermal effects (radiation pressure effect, radiometer effect) that will appear in the Inertial Sensor environment of the LTP (LISA Technology Package). We report as well how these experiments are going to be implemented in the LTPDA toolbox, which is a dedicated tool for LPF data analysis that will allow full traceability and reproducibility of the analysis thanks to complete recording of the processes.

Inflight magnetic characterization of the test masses onboard LISA Pathfinder

LISA Pathfinder is a science and technology demonstrator of the European Space Agency within the framework of its LISA mission, the latter aiming 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. The diagnostics subsystem consists of several modules, one of which is the magnetic diagnostics unit. Its main function is the assessment of the differential acceleration noise between the test masses due to magnetic effects. This subsystem is composed of two onboard coils intended to produce controlled magnetic fields at the location of the test masses. These magnetic fields couple with the remanent magnetic moment and susceptibility and produce forces and torques on the test masses. These, in turn, produce kinematic excursions of the test masses which are sensed by the onboard interferometer. We prove that adequately processing these excursions, the magnetic properties of the test masses can be estimated using classical multi-parameter estimation techniques. Moreover, we show that special processing procedures to minimize the effect of the multi channel cross-talks are needed. Finally, we demonstrate that the quality of our estimates is frequency dependent. We also suggest that using a multiple frequency experiment the global estimate can be obtained in such a way that the results of the magnetic experiment are more reliable. Finally, using our procedure we compute the the contribution of the magnetic noise to the total proof-mass acceleration noise.

Design of the magnetic diagnostics unit onboard LISA Pathfinder

LISA (Laser Interferometer Space Antenna) is a joint mission of ESA and NASA which aims to be the first space-borne gravitational wave observatory. Due to the high complexity and technological challenges that LISA will face, ESA decided to launch a technological demonstrator, LISA Pathfinder. The payload of LISA Pathfinder is the so-called LISA Technology Package, 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 (geodesic motion). The magnetic, thermal and radiation disturbances affecting the payload are monitored and dealt by the diagnostics subsystem. The diagnostics subsystem consists of several modules, and one of these is the magnetic diagnostics unit. Its main function is the assessment of differential acceleration noise between test masses due to the magnetic effects. To do so, it has to determine the magnetic characteristics of the test masses, namely their magnetic remanences and susceptibilities. In this paper we show how this can be achieved to the desired accuracy.

LISA Pathfinder data analysis

As the launch of LISA Pathfinder (LPF) draws near, more and more effort is being put in to the preparation of the data analysis activities that will be carried out during the mission operations. The operations phase of the mission will be composed of a series of experiments that will be carried out on the satellite. These experiments will be directed and analysed by the data analysis team, which is part of the operations team. The operations phase will last about 90 days, during which time the data analysis team aims to fully characterize the LPF, and in particular, its core instrument the LISA Technology Package. By analysing the various couplings present in the system, the different noise sources that will disturb the system, and through the identification of the key physical parameters of the system, a detailed noise budget of the instrument will be constructed that will allow the performance of the different subsystems to be assessed and projected towards LISA. This paper describes the various aspects of the full data analysis chain that are needed to successfully characterize the LPF and build up the noise budget during mission operations.

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

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.

Bayesian parameter estimation in the second LISA Pathfinder mock data challenge

A main scientific output of the LISA Pathfinder mission is to provide a noise model that can be extended to the future gravitational wave observatory, LISA. The success of the mission depends thus upon a deep understanding of the instrument, especially the ability to correctly determine the parameters of the underlying noise model. In this work we estimate the parameters of a simplified model of the LISA Technology Package (LTP) instrument. We describe the LTP by means of a closed-loop model that is used to generate the data, both injected signals and noise. Then, parameters are estimated using a Bayesian framework and it is shown that this method reaches the optimal attainable error, the Cramer-Rao bound. We also address an important issue for the mission: how to efficiently combine the results of different experiments to obtain a unique set of parameters describing the instrument.

Calibrating spectral estimation for the LISA Technology Package with multichannel synthetic noise generation

The scientific objectives of the Lisa Technology Package (LTP) experiment, on board of the LISA Pathfinder mission, demand for an accurate calibration and validation of the data analysis tools in advance of the mission launch. The levels of confidence required on the mission outcomes can be reached only with an intense activity on synthetically generated data. A flexible procedure allowing the generation of cross-correlated stationary noise time series was set-up. Multi-channel time series with the desired cross correlation behavior can be generated once a model for a multichannel cross-spectral matrix is provided. The core of the procedure is the synthesis of a noise coloring multichannel filter through a frequency-by-frequency eigendecomposition of the model cross-spectral matrix and a Z-domain fit. The common problem of initial transients in noise time series is solved with a proper initialization of the filter recursive equations. The noise generator performances were tested in a two dimensional case study of the LTP dynamics along the two principal channels of the sensing interferometer.

The magnetic diagnostics subsystem of the LISA Technology Package

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.

Proton irradiation test on the flight model radiation monitor for LISA Pathfinder

The design of the Radiation Monitor in the LISA Technology Package on board LISA Pathfinder is based on two silicon PIN diodes, placed parallel to each other in a telescopic configuration. One of them will be able to record spectral information of the particle hitting the diode. A test campaign for the Flight Model Radiation Monitor is proposed to verify its performance. This paper shows the results obtained with a simulated flight model geometry using GEANT4, to be compared with the real data that will be obtained in a proton irradiation facility.

Theory and modelling of the magnetic field measurement in LISA PathFinder

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 their respective positions. However, their readouts do not provide a direct measurement of the magnetic field at the positions of the test masses, and hence an interpolation method must be designed and implemented to obtain the values of the magnetic field at these positions. However, such an interpolation process faces serious difficulties. Indeed, the size of the interpolation region is excessive for a linear interpolation to be reliable while, on the other hand, the number of magnetometer channels do not provide sufficient data to go beyond the linear approximation. We describe an alternative method to address this issue, by means of neural network algorithms. The key point in this approach is the ability of neural networks to learn from suitable training data representing the behaviour of the magnetic field. Despite the relatively large distance between the test masses and the magnetometers, and the insufficient number of data channels, we find that our artificial neural network algorithm is able to reduce the estimation errors of the field and gradient down to levels below 10%, a quite satisfactory result. Learning efficiency can be best improved by making use of data obtained in on-ground measurements prior to mission launch in all relevant satellite locations and in real operation conditions. Reliable information on that appears to be essential for a meaningful assessment of magnetic noise in the LTP.