63844509
The LISA Pathfinder interferometry-hardware and system testing
Preparations for the LISA Pathfinder mission have reached an exciting stage. Tests of the engineering model (EM) of the optical metrology system have recently been completed at the Albert Einstein Institute, Hannover, and flight model tests are now underway. Significantly, they represent the first complete integration and testing of the space-qualified hardware and are the first tests on an optical system level. The results and test procedures of these campaigns will be utilized directly in the ground-based flight hardware tests, and subsequently during in-flight operations. In addition, they allow valuable testing of the data analysis methods using the MATLAB-based LTP data analysis toolbox. This paper presents an overview of the results from the EM test campaign that was successfully completed in December 2009.
122302793
Superfluid /sup 4/He analog of the rf superconducting quantum interference device and the detection of inertial and gravitational fields
Superfluid /sup 4/He filling a thin torus, interrupted by N weak links in series, is found to give an angular momentum contribution which depends parametrically on the steady absolute rotation of the torus. This in principle enables one to determine off-diagonal components of the local metric. Recent claims for a surprisingly high sensitivity of a similar system to gravitational waves and Lense-Thirring fields are unfortunately ill founded.
118459662
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 instrument. We describe the LISA Technology Package 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.
119205701
Discrete derivative estimation in LISA Pathfinder data reduction
Data analysis for the LISA Technology package (LTP) experiment to be flown aboard the LISA Pathfinder mission requires the solution of the system dynamics for the calculation of the force acting on the test masses (TMs) starting from interferometer position data. The need for a solution to this problem has prompted us to implement a discrete time-domain derivative estimator suited for the LTP experiment requirements. We first report on the mathematical procedures for the definition of two methods; the first based on a parabolic fit approximation and the second based on a Taylor series expansion. These two methods are then generalized and incorporated into a more general class of five-point discrete derivative estimators. The same procedure employed for the second derivative can be applied to the estimation of the first derivative and of a data smoother allowing defining a class of simple five-point estimators for both. The performances of three particular realizations of the five-point second-derivative estimator are analyzed with simulated noisy data. This analysis pointed out that those estimators introducing large amount of high-frequency noise can determine systematic errors in the estimation of low-frequency noise levels.
52000719
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.
122439729
APPROXIMATE GRAVITATIONAL WAVES VIA DEFORMATIONS OF EMBEDDINGS
Many Einstein spaces can be embedded globally in pseudo-Euclidean spaces of dimension N > 4. The geometrical quantities characterizing the embedded manifold can be expressed by means of derivatives of the embedding functions zA (xµ), A, B, = 1, 2, …N, µ, ν, …= 0, 1, 2, 3. An infinitesimal deformation of embedding can be expanded into a series ${\tilde{z}}^A = z^A (x^{\mu}) + \varepsilon v^A \, (x^{\mu}) + \varepsilon^2 w^A \, (x^{\mu}) +.$, giving rise to a similar expansion of geometrical quantities of the embedded Einstein space, and the Einstein equations in vacuo, too. We show how gravitational wave solutions appear naturally in this context.
118385730
Testing general relativity using gravitational waves from binary neutron stars: Effect of spins
We present a Bayesian data analysis pipeline for testing GR using gravitational wave signals from coalescing compact binaries, and in particular binary neutron stars. In this study, we investigate its performance when sources with spins are taken into account.
9403518
Measuring the spin of black holes in binary systems using gravitational waves.
Compact binary coalescences are the most promising sources of gravitational waves (GWs) for ground-based detectors. Binary systems containing one or two spinning black holes are particularly interesting due to spin-orbit (and eventual spin-spin) interactions and the opportunity of measuring spins directly through GW observations. In this Letter, we analyze simulated signals emitted by spinning binaries with several values of masses, spins, orientations, and signal-to-noise ratios, as detected by an advanced LIGO-Virgo network. We find that for moderate or high signal-to-noise ratio the spin magnitudes can be estimated with errors of a few percent (5%-30%) for neutron star-black hole (black hole-black hole) systems. Spins' tilt angle can be estimated with errors of 0.04 rad in the best cases, but typical values will be above 0.1 rad. Errors will be larger for signals barely above the threshold for detection. The difference in the azimuth angles of the spins, which may be used to check if spins are locked into resonant configurations, cannot be constrained. We observe that the best performances are obtained when the line of sight is perpendicular to the system's total angular momentum and that a sudden change of behavior occurs when a system is observed from angles such that the plane of the orbit can be seen both from above and below during the time the signal is in band. This study suggests that direct measurement of black hole spin by means of GWs can be as precise as what can be obtained from x-ray binaries.
1506194
TIGER: A data analysis pipeline for testing the strong-field dynamics of general relativity with gravitational wave signals from coalescing compact binaries
The direct detection of gravitational waves with upcoming second-generation gravitational wave observatories such as Advanced LIGO and Advanced Virgo will allow us to probe the genuinely strong-field dynamics of general relativity (GR) for the first time. We have developed a data analysis pipeline called TIGER (test infrastructure for general relativity), which uses signals from compact binary coalescences to perform a model-independent test of GR. In this paper we focus on signals from coalescing binary neutron stars, for which sufficiently accurate waveform models are already available which can be generated fast enough on a computer that they can be used in Bayesian inference. By performing numerical experiments in stationary, Gaussian noise, we show that for such systems, TIGER is robust against a number of unmodeled fundamental, astrophysical, and instrumental effects, such as differences between waveform approximants, a limited number of post-Newtonian phase contributions being known, the effects of neutron star tidal deformability on the orbital motion, neutron star spins, and instrumental calibration errors.
122803437
Effect of calibration errors on Bayesian parameter estimation for gravitational wave signals from inspiral binary systems in the advanced detectors era: Further investigations
By 2015, the advanced versions of the gravitational wave detectors Virgo and LIGO will be online. They will collect data in coincidence with enough sensitivity to potentially deliver multiple detections of gravitational waves from inspirals of compact-object binaries. In a previous work, we have studied the effects introduced in the estimation of the physical parameters of the source by uncertainties in the calibration of the interferometers. Our bias estimator for parameter errors introduced by calibration uncertainties consisted of two terms: A genuine bias due to the calibration errors, and a contribution coming from the limited number of samples used to explore the parameter space. In this article, we have focused on this second term, and we have shown how it is smaller than the former (about 10 times smaller), and how it decreases as the signal-to-noise ratio increases.