57 research outputs found
Catching Super Massive Black Hole Binaries Without a Net
The gravitational wave signals from coalescing Supermassive Black Hole
Binaries are prime targets for the Laser Interferometer Space Antenna (LISA).
With optimal data processing techniques, the LISA observatory should be able to
detect black hole mergers anywhere in the Universe. The challenge is to find
ways to dig the signals out of a combination of instrument noise and the large
foreground from stellar mass binaries in our own galaxy. The standard procedure
of matched filtering against a grid of templates can be computationally
prohibitive, especially when the black holes are spinning or the mass ratio is
large. Here we develop an alternative approach based on Metropolis-Hastings
sampling and simulated annealing that is orders of magnitude cheaper than a
grid search. We demonstrate our approach on simulated LISA data streams that
contain the signals from binary systems of Schwarzschild Black Holes, embedded
in instrument noise and a foreground containing 26 million galactic binaries.
The search algorithm is able to accurately recover the 9 parameters that
describe the black hole binary without first having to remove any of the bright
foreground sources, even when the black hole system has low signal-to-noise.Comment: 4 pages, 3 figures, Refined search algorithm, added low SNR exampl
Coherent Bayesian analysis of inspiral signals
We present in this paper a Bayesian parameter estimation method for the
analysis of interferometric gravitational wave observations of an inspiral of
binary compact objects using data recorded simultaneously by a network of
several interferometers at different sites. We consider neutron star or black
hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and
2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo
methods that are adapted in order to efficiently explore the particular
parameter space. Examples are shown to illustrate how and what information
about the different parameters can be derived from the data. This study uses
simulated signals and data with noise characteristics that are assumed to be
defined by the LIGO and Virgo detectors operating at their design
sensitivities. Nine parameters are estimated, including those associated with
the binary system, plus its location on the sky. We explain how this technique
will be part of a detection pipeline for binary systems of compact objects with
masses up to 20 \sunmass, including cases where the ratio of the individual
masses can be extreme.Comment: Accepted for publication in Classical and Quantum Gravity, Special
issue for GWDAW-1
Random template placement and prior information
In signal detection problems, one is usually faced with the task of searching
a parameter space for peaks in the likelihood function which indicate the
presence of a signal. Random searches have proven to be very efficient as well
as easy to implement, compared e.g. to searches along regular grids in
parameter space. Knowledge of the parameterised shape of the signal searched
for adds structure to the parameter space, i.e., there are usually regions
requiring to be densely searched while in other regions a coarser search is
sufficient. On the other hand, prior information identifies the regions in
which a search will actually be promising or may likely be in vain. Defining
specific figures of merit allows one to combine both template metric and prior
distribution and devise optimal sampling schemes over the parameter space. We
show an example related to the gravitational wave signal from a binary inspiral
event. Here the template metric and prior information are particularly
contradictory, since signals from low-mass systems tolerate the least mismatch
in parameter space while high-mass systems are far more likely, as they imply a
greater signal-to-noise ratio (SNR) and hence are detectable to greater
distances. The derived sampling strategy is implemented in a Markov chain Monte
Carlo (MCMC) algorithm where it improves convergence.Comment: Proceedings of the 8th Edoardo Amaldi Conference on Gravitational
Waves. 7 pages, 4 figure
Atomic correlations in itinerant ferromagnets: quasi-particle bands of nickel
We measure the band structure of nickel along various high-symmetry lines of
the bulk Brillouin zone with angle-resolved photoelectron spectroscopy. The
Gutzwiller theory for a nine-band Hubbard model whose tight-binding parameters
are obtained from non-magnetic density-functional theory resolves most of the
long-standing discrepancies between experiment and theory on nickel. Thereby we
support the view of itinerant ferromagnetism as induced by atomic correlations.Comment: 4 page REVTeX 4.0, one figure, one tabl
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
We investigate the Bayesian framework for detection of continuous
gravitational waves (GWs) in the context of targeted searches, where the phase
evolution of the GW signal is assumed to be known, while the four amplitude
parameters are unknown. We show that the orthodox maximum-likelihood statistic
(known as F-statistic) can be rediscovered as a Bayes factor with an unphysical
prior in amplitude parameter space. We introduce an alternative detection
statistic ("B-statistic") using the Bayes factor with a more natural amplitude
prior, namely an isotropic probability distribution for the orientation of GW
sources. Monte-Carlo simulations of targeted searches show that the resulting
Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e. has a
higher expected detection probability at equal false-alarm probability) than
the frequentist F-statistic.Comment: 12 pages, presented at GWDAW13, to appear in CQ
Studying stellar binary systems with the Laser Interferometer Space Antenna using Delayed Rejection Markov chain Monte Carlo methods
Bayesian analysis of LISA data sets based on Markov chain Monte Carlo methods
has been shown to be a challenging problem, in part due to the complicated
structure of the likelihood function consisting of several isolated local
maxima that dramatically reduces the efficiency of the sampling techniques.
Here we introduce a new fully Markovian algorithm, a Delayed Rejection
Metropolis-Hastings Markov chain Monte Carlo method, to efficiently explore
these kind of structures and we demonstrate its performance on selected LISA
data sets containing a known number of stellar-mass binary signals embedded in
Gaussian stationary noise.Comment: 12 pages, 4 figures, accepted in CQG (GWDAW-13 proceedings
An evaluation of Bradfordizing effects
The purpose of this paper is to apply and evaluate the bibliometric method Bradfordizing for information retrieval (IR) experiments. Bradfordizing is used for generating core document sets for subject-specific questions and to reorder result sets from distributed searches. The method will be applied and tested in a controlled scenario of scientific literature databases from social and political sciences, economics, psychology and medical science (SOLIS, SoLit, USB Köln Opac, CSA Sociological Abstracts, World Affairs Online, Psyndex and Medline) and 164 standardized topics. An evaluation of the method and its effects is carried out in two laboratory-based information retrieval experiments (CLEF and KoMoHe) using a controlled document corpus and human relevance assessments. The results show that Bradfordizing is a very robust method for re-ranking the main document types (journal articles and monographs) in today’s digital libraries (DL). The IR tests show that relevance distributions after re-ranking improve at a significant level if articles in the core are compared with articles in the succeeding zones. The items in the core are significantly more often assessed as relevant, than items in zone 2 (z2) or zone 3 (z3). The improvements between the zones are statistically significant based on the Wilcoxon signed-rank test and the paired T-Test
Inference on inspiral signals using LISA MLDC data
In this paper we describe a Bayesian inference framework for analysis of data
obtained by LISA. We set up a model for binary inspiral signals as defined for
the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte
Carlo (MCMC) algorithm to facilitate exploration and integration of the
posterior distribution over the 9-dimensional parameter space. Here we present
intermediate results showing how, using this method, information about the 9
parameters can be extracted from the data.Comment: Accepted for publication in Classical and Quantum Gravity, GWDAW-11
special issu
Sensor data classification for the indication of lameness in sheep
Lameness is a vital welfare issue in most sheep farming countries, including the UK. The pre-detection at the farm level could prevent the disease from becoming chronic. The development of wearable sensor technologies enables the idea of remotely monitoring the changes in animal movements which relate to lameness. In this study, 3D-acceleration, 3D-orientation, and 3D-linear acceleration sensor data were recorded at ten samples per second via the sensor attached to sheep neck collar. This research aimed to determine the best accuracy among various supervised machine learning techniques which can predict the early signs of lameness while the sheep are walking on a flat field. The most influencing predictors for lameness indication were also addressed here. The experimental results revealed that the Decision Tree classifier has the highest accuracy of 75.46%, and the orientation sensor data (angles) around the neck are the strongest predictors to differentiate among severely lame, mildly lame and sound classes of sheep
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