Use of the MultiNest algorithm for gravitational wave data analysis

We describe an application of the MultiNest algorithm to gravitational wave data analysis. MultiNest is a multimodal nested sampling algorithm designed to efficiently evaluate the Bayesian evidence and return posterior probability densities for likelihood surfaces containing multiple secondary modes. The algorithm employs a set of live points which are updated by partitioning the set into multiple overlapping ellipsoids and sampling uniformly from within them. This set of live points climbs up the likelihood surface through nested iso-likelihood contours and the evidence and posterior distributions can be recovered from the point set evolution. The algorithm is model-independent in the sense that the specific problem being tackled enters only through the likelihood computation, and does not change how the live point set is updated. In this paper, we consider the use of the algorithm for gravitational wave data analysis by searching a simulated LISA data set containing two non-spinning supermassive black hole binary signals. The algorithm is able to rapidly identify all the modes of the solution and recover the true parameters of the sources to high precision.Comment: 18 pages, 4 figures, submitted to Class. Quantum Grav; v2 includes various changes in light of referee's comment

Modeling Mobility Engineering in a Theater Level Combat Model

This thesis describes the development of a methodology to model theater-level mobility engineering assets in the Joint Staff\u27s Joint Warfare Analysis Experimental Prototype (JWAEP) and to quantify the joint and Army doctrine that guides the task organization of engineers for combat and which quantifies engineer mobility effects in combat. The methodology incorporates theater-level mobility engineering assets into the JWAEP by using Mission, Enemy, Troops available, Terrain, and Time (MET-T principles which reflect joint and Army doctrine, and combines them with the existing basic concepts in other theater-level models. Additional aspects of the problem include determining the manmade and natural obstacles\u27 delay and attrition effects, determining the obstacle intelligence acquisition procedures, identifying solution techniques, verifying the results, and making recommendations

A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques

We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of ~ 10^4 with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves.Comment: Further checks about accuracy of neural network approximation, fixed typos, added refs. Main results unchanged. Matches version accepted by JHE

Recent advances in low oxidation state aluminium chemistry

Documenting the synthesis and isolation of novel low oxidation state aluminium (Al) compounds, which until recently has seen relatively slow progress over the 30 years since such species were first isolated

Aluminum Amidinates: Insights into Alkyne Hydroboration.

The mechanism of the aluminum-mediated hydroboration of terminal alkynes was investigated using a series of novel aluminum amidinate hydride and alkyl complexes bearing symmetric and asymmetric ligands. The new aluminum complexes were fully characterized and found to facilitate the formation of the (E)-vinylboronate hydroboration product, with rates and orders of reaction linked to complex size and stability. Kinetic analysis and stoichiometric reactions were used to elucidate the mechanism, which we propose to proceed via the initial formation of an Al-borane adduct. Additionally, the most unstable complex was found to promote decomposition of the pinacolborane substrate to borane (BH3), which can then proceed to catalyze the reaction. This mechanism is in contrast to previously reported aluminum hydride-catalyzed hydroboration reactions, which are proposed to proceed via the initial formation of an aluminum acetylide, or by hydroalumination to form a vinylboronate ester as the first step in the catalytic cycle

Bayes-X: a Bayesian inference tool for the analysis of X-ray observations of galaxy clusters

We present the first public release of our Bayesian inference tool, Bayes-X, for the analysis of X-ray observations of galaxy clusters. We illustrate the use of Bayes-X by analysing a set of four simulated clusters at z=0.2-0.9 as they would be observed by a Chandra-like X-ray observatory. In both the simulations and the analysis pipeline we assume that the dark matter density follows a spherically-symmetric Navarro, Frenk and White (NFW) profile and that the gas pressure is described by a generalised NFW (GNFW) profile. We then perform four sets of analyses. By numerically exploring the joint probability distribution of the cluster parameters given simulated Chandra-like data, we show that the model and analysis technique can robustly return the simulated cluster input quantities, constrain the cluster physical parameters and reveal the degeneracies among the model parameters and cluster physical parameters. We then analyse Chandra data on the nearby cluster, A262, and derive the cluster physical profiles. To illustrate the performance of the Bayesian model selection, we also carried out analyses assuming an Einasto profile for the matter density and calculated the Bayes factor. The results of the model selection analyses for the simulated data favour the NFW model as expected. However, we find that the Einasto profile is preferred in the analysis of A262. The Bayes-X software, which is implemented in Fortran 90, is available at http://www.mrao.cam.ac.uk/facilities/software/bayesx/.Comment: 22 pages, 11 figure

The development and sea trials of a subsea holographic camera for large volume in-situ recording of marine organisms

We describe the development, construction and sea testing of an underwater holographic camera (HoloCam) for in situ recording of marine organisms and particles in large volumes of sea water. HoloCam comprises a laser, power supply, holographic recording optics and plate holders, a water-tight housing and a support frame. Added to this are control electronics such that the entire camera is remotely operable and controllable from ship or dock-side. Uniquely the camera can simultaneously record both in-line and off-axis holograms using a pulsed frequency doubled Nd-YAG laser. In-line holography is capable of producing images of organisms with a resolution of better than 10 Pm (at concentrations up to a few thousand per cubic centimetre at the smallest sizes). Off-axis holograms of aquatic systems of up to 50,000 cm3 volume, have been recorded. Following initial laboratory testing, the holo-camera was evaluated in an observation tank and ultimately was tested in Loch Etive, Scotland. In-line and off-axis holograms were recorded to a depth of 100 m. We will present results on the test dives and evaluation of the camera performance