129,058 research outputs found
Model selection in cosmology
Model selection aims to determine which theoretical models are most plausible given some data, without necessarily considering preferred values of model parameters. A common model selection question is to ask when new data require introduction of an additional parameter, describing a newly discovered physical effect. We review model selection statistics, then focus on the Bayesian evidence, which implements Bayesian analysis at the level of models rather than parameters. We describe our CosmoNest code, the first computationally efficient implementation of Bayesian model selection in a cosmological context. We apply it to recent WMAP satellite data, examining the need for a perturbation spectral index differing from the scaleinvariant (Harrison–Zel'dovich) case
Classification of Jaw Bone Cysts and Necrosis via the Processing of Orthopantomograms
The authors analyze the design of a method for automatized evaluation of parameters in orthopantomographic images capturing pathological tissues developed in human jaw bones. The main problem affecting the applied medical diagnostic procedures consists in low repeatability of the performed evaluation. This condition is caused by two aspects, namely subjective approach of the involved medical specialists and the related exclusion of image processing instruments from the evaluation scheme. The paper contains a description of the utilized database containing images of cystic jaw bones; this description is further complemented with appropriate schematic repre¬sentation. Moreover, the authors present the results of fast automatized segmentation realized via the live-wire method and compare the obtained data with the results provided by other segmentation techniques. The shape parameters and the basic statistical quantities related to the distribution of intensities in the segmented areas are selected. The evaluation results are provided in the final section of the study; the authors correlate these values with the subjective assessment carried out by radiologists. Interestingly, the paper also comprises a discussion presenting the possibility of using selected parameters or their combinations to execute automatic classification of cysts and osteonecrosis. In this context, a comparison of various classifiers is performed, including the Decision Tree, Naive Bayes, Neural Network, k-NN, SVM, and LDA classifica¬tion tools. Within this comparison, the highest degree of accuracy (85% on the average) can be attributed to the Decision Tree, Naive Bayes, and Neural Network classifier
The Band Excitation Method in Scanning Probe Microscopy for Rapid Mapping of Energy Dissipation on the Nanoscale
Mapping energy transformation pathways and dissipation on the nanoscale and
understanding the role of local structure on dissipative behavior is a
challenge for imaging in areas ranging from electronics and information
technologies to efficient energy production. Here we develop a novel Scanning
Probe Microscopy (SPM) technique in which the cantilever is excited and the
response is recorded over a band of frequencies simultaneously rather than at a
single frequency as in conventional SPMs. This band excitation (BE) SPM allows
very rapid acquisition of the full frequency response at each point (i.e.
transfer function) in an image and in particular enables the direct measurement
of energy dissipation through the determination of the Q-factor of the
cantilever-sample system. The BE method is demonstrated for force-distance and
voltage spectroscopies and for magnetic dissipation imaging with sensitivity
close to the thermomechanical limit. The applicability of BE for various SPMs
is analyzed, and the method is expected to be universally applicable to all
ambient and liquid SPMs.Comment: 32 pages, 9 figures, accepted for publication in Nanotechnolog
Astrometric signal profile fitting for Gaia
A tool for representation of the one-dimensional astrometric signal of Gaia
is described and investigated in terms of fit discrepancy and astrometric
performance with respect to number of parameters required. The proposed basis
function is based on the aberration free response of the ideal telescope and
its derivatives, weighted by the source spectral distribution. The influence of
relative position of the detector pixel array with respect to the optical image
is analysed, as well as the variation induced by the source spectral emission.
The number of parameters required for micro-arcsec level consistency of the
reconstructed function with the detected signal is found to be 11. Some
considerations are devoted to the issue of calibration of the instrument
response representation, taking into account the relevant aspects of source
spectrum and focal plane sampling. Additional investigations and other
applications are also suggested.Comment: 13 pages, 21 figures, Accepted by MNRAS 2010 January 29. Received
2010 January 28; in original form 2009 September 3
Bayesian methods of astronomical source extraction
We present two new source extraction methods, based on Bayesian model
selection and using the Bayesian Information Criterion (BIC). The first is a
source detection filter, able to simultaneously detect point sources and
estimate the image background. The second is an advanced photometry technique,
which measures the flux, position (to sub-pixel accuracy), local background and
point spread function. We apply the source detection filter to simulated
Herschel-SPIRE data and show the filter's ability to both detect point sources
and also simultaneously estimate the image background. We use the photometry
method to analyse a simple simulated image containing a source of unknown flux,
position and point spread function; we not only accurately measure these
parameters, but also determine their uncertainties (using Markov-Chain Monte
Carlo sampling). The method also characterises the nature of the source
(distinguishing between a point source and extended source). We demonstrate the
effect of including additional prior knowledge. Prior knowledge of the point
spread function increase the precision of the flux measurement, while prior
knowledge of the background has onlya small impact. In the presence of higher
noise levels, we show that prior positional knowledge (such as might arise from
a strong detection in another waveband) allows us to accurately measure the
source flux even when the source is too faint to be detected directly. These
methods are incorporated in SUSSEXtractor, the source extraction pipeline for
the forthcoming Akari FIS far-infrared all-sky survey. They are also
implemented in a stand-alone, beta-version public tool that can be obtained at
http://astronomy.sussex.ac.uk/rss23/sourceMiner\_v0.1.2.0.tar.gzComment: Accepted for publication by ApJ (this version compiled used
emulateapj.cls
The Spine of the Cosmic Web
We present the SpineWeb framework for the topological analysis of the Cosmic
Web and the identification of its walls, filaments and cluster nodes. Based on
the watershed segmentation of the cosmic density field, the SpineWeb method
invokes the local adjacency properties of the boundaries between the watershed
basins to trace the critical points in the density field and the separatrices
defined by them. The separatrices are classified into walls and the spine, the
network of filaments and nodes in the matter distribution. Testing the method
with a heuristic Voronoi model yields outstanding results. Following the
discussion of the test results, we apply the SpineWeb method to a set of
cosmological N-body simulations. The latter illustrates the potential for
studying the structure and dynamics of the Cosmic Web.Comment: Accepted for publication HIGH-RES version:
http://skysrv.pha.jhu.edu/~miguel/SpineWeb
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