Bounding the mass of the graviton using binary pulsar observations

The close agreement between the predictions of dynamical general relativity for the radiated power of a compact binary system and the observed orbital decay of the binary pulsars PSR B1913+16 and PSR B1534+12 allows us to bound the graviton mass to be less than 7.6 x 10^{-20} eV with 90% confidence. This bound is the first to be obtained from dynamic, as opposed to static-field, relativity. The resulting limit on the graviton mass is within two orders of magnitude of that from solar system measurements, and can be expected to improve with further observations.Comment: 16 pages, 1 figure. Added appendix on other choices for mass ter

Identifying the Host Galaxy of Gravitational Wave Signals

One of the goals of the current LIGO-GEO-Virgo science run is to identify transient gravitational wave (GW) signals in near real time to allow follow-up electromagnetic (EM) observations. An EM counterpart could increase the confidence of the GW detection and provide insight into the nature of the source. Current GW-EM campaigns target potential host galaxies based on overlap with the GW sky error box. We propose a new statistic to identify the most likely host galaxy, ranking galaxies based on their position, distance, and luminosity. We test our statistic with Monte Carlo simulations of GWs produced by coalescing binaries of neutron stars (NS) and black holes (BH), one of the most promising sources for ground-based GW detectors. Considering signals accessible to current detectors, we find that when imaging a single galaxy, our statistic correctly identifies the true host ~20% to ~50% of the time, depending on the masses of the binary components. With five narrow-field images the probability of imaging the true host increases to ~50% to ~80%. When collectively imaging groups of galaxies using large field-of-view telescopes, the probability improves to ~30% to ~60% for a single image and to ~70% to ~90% for five images. For the advanced generation of detectors (c. 2015+), and considering binaries within 100 Mpc (the reach of the galaxy catalogue used), the probability is ~40% for one narrow-field image, ~75% for five narrow-field images, ~65% for one wide-field image, and ~95% for five wide-field images, irrespective of binary type.Comment: 5 pages, 2 figure

Swift Pointing and Gravitational-Wave Bursts from Gamma-Ray Burst Events

The currently accepted model for gamma-ray burst phenomena involves the violent formation of a rapidly rotating solar-mass black hole. Gravitational waves should be associated with the black-hole formation, and their detection would permit this model to be tested. Even upper limits on the gravitational-wave strength associated with gamma-ray bursts could constrain the gamma-ray burst model. This requires joint observations of gamma-ray burst events with gravitational and gamma-ray detectors. Here we examine how the quality of an upper limit on the gravitational-wave strength associated with gamma-ray bursts depends on the relative orientation of the gamma-ray-burst and gravitational-wave detectors, and apply our results to the particular case of the Swift Burst-Alert Telescope (BAT) and the LIGO gravitational-wave detectors. A result of this investigation is a science-based ``figure of merit'' that can be used, together with other mission constraints, to optimize the pointing of the Swift telescope for the detection of gravitational waves associated with gamma-ray bursts.Comment: iop style, 1 figure, 6 pages, presented at GWDAW 200

The MSFC space station/space operations mechanism test bed

The Space Station/Space Operations Mechanism Test Bed consists of the following: a hydraulically driven, computer controlled Six Degree-of-Freedom Motion System (6DOF); a six degree-of-freedom force and moment sensor; remote driving stations with computer generated or live TV graphics; and a parallel digital processor that performs calculations to support the real time simulation. The function of the Mechanism Test Bed is to test docking and berthing mechanisms for Space Station Freedom and other orbiting space vehicles in a real time, hardware-in-the-loop simulation environment. Typically, the docking and berthing mechanisms are composed of two mating components, one for each vehicle. In the facility, one component is attached to the motion system, while the other component is mounted to the force/moment sensor fixed in the support structure above the 6DOF. The six components of the contact forces/moments acting on the test article and its mating component are measured by the force/moment sensor

Bayesian Inference Analysis of Unmodelled Gravitational-Wave Transients

We report the results of an in-depth analysis of the parameter estimation capabilities of BayesWave, an algorithm for the reconstruction of gravitational-wave signals without reference to a specific signal model. Using binary black hole signals, we compare BayesWave's performance to the theoretical best achievable performance in three key areas: sky localisation accuracy, signal/noise discrimination, and waveform reconstruction accuracy. BayesWave is most effective for signals that have very compact time-frequency representations. For binaries, where the signal time-frequency volume decreases with mass, we find that BayesWave's performance reaches or approaches theoretical optimal limits for system masses above approximately 50 M_sun. For such systems BayesWave is able to localise the source on the sky as well as templated Bayesian analyses that rely on a precise signal model, and it is better than timing-only triangulation in all cases. We also show that the discrimination of signals against glitches and noise closely follow analytical predictions, and that only a small fraction of signals are discarded as glitches at a false alarm rate of 1/100 y. Finally, the match between BayesWave- reconstructed signals and injected signals is broadly consistent with first-principles estimates of the maximum possible accuracy, peaking at about 0.95 for high mass systems and decreasing for lower-mass systems. These results demonstrate the potential of unmodelled signal reconstruction techniques for gravitational-wave astronomy.Comment: 10 pages, 7 figure

Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal, and they may fail in the presence of transient non-stationarities, which are common in real detectors. In order to address this problem we introduce a consistency test that is robust against noise non-stationarities and allows one to distinguish between gravitational-wave bursts and noise transients. This technique does not require any a priori knowledge of the putative burst waveform.Comment: 18 pages, 11 figures; corrected corrupted figur

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

NASA MSFC hardware in the loop simulations of automatic rendezvous and capture systems

Two complementary hardware-in-the-loop simulation facilities for automatic rendezvous and capture systems at MSFC are described. One, the Flight Robotics Laboratory, uses an 8 DOF overhead manipulator with a work volume of 160 by 40 by 23 feet to evaluate automatic rendezvous algorithms and range/rate sensing systems. The other, the Space Station/Station Operations Mechanism Test Bed, uses a 6 DOF hydraulic table to perform docking and berthing dynamics simulations