8,310 research outputs found
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
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