Functional Dynamics of Proteins Elucidated by Statistical Analysis of Simulation Data

AbstractWe present a statistical analysis of the interactions between satellite galaxies in cosmological dark matter halos taken from fully self-consistent high-resolution simulations of galaxy clusters. We show that the number distribution of satellite encounters has a tail that extends to as many as three to four encounters per orbit. On average 30% of the substructure population had at least one encounter (per orbit) with another satellite galaxy. However, this result depends on the age of the dark matter host halo with a clear trend for more interactions in younger systems. We also report a correlation between the number of encounters and the distance of the satellites to the centre of the cluster — satellite galaxies closer to the centre experience more interactions. However, this can be simply explained by the radial distribution of the substructure population and merely reflects the fact that the density of satellites is higher in those regions.In order to find substructure galaxies we applied (and present) a new technique based upon theN-body code MLAPM. This new halo finder MHF (MLAPM’s halo finder) acts with exactly the same accuracy as theN-body code itself and is therefore free of any bias and spurious mismatch between simulation data and halo finding precision related to numerical effects.

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Feature-Based Statistical Analysis of Combustion Simulation Data

IEEE Transactions on Visualization and Computer Graphics ◽

10.1109/tvcg.2011.199 ◽

2011 ◽

Vol 17 (12) ◽

pp. 1822-1831 ◽

Cited By ~ 24

Author(s):

Janine C. Bennett ◽

Vaidyanathan Krishnamoorthy ◽

Shusen Liu ◽

Ray W. Grout ◽

Evatt R. Hawkes ◽

...

Keyword(s):

Statistical Analysis ◽

Simulation Data ◽

Combustion Simulation ◽

Feature Based

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In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units

10.2172/1093099 ◽

2013 ◽

Author(s):

Niloo Ranjan ◽

Jibonananda Sanyal ◽

Joshua Ryan New

Keyword(s):

Statistical Analysis ◽

Simulation Data ◽

Graphical Processing Units ◽

Graphical Processing

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Statistical Analysis of Simulation Data

Handbook of Monte Carlo Methods - Wiley Series in Probability and Statistics ◽

10.1002/9781118014967.ch8 ◽

2011 ◽

pp. 301-345 ◽

Cited By ~ 1

Keyword(s):

Statistical Analysis ◽

Simulation Data

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Statistical analysis of classification

Symposium - International Astronomical Union ◽

10.1017/s0074180900053420 ◽

1966 ◽

Vol 24 ◽

pp. 188-189

Author(s):

T. J. Deeming

Keyword(s):

Multivariate Analysis ◽

Statistical Analysis ◽

Classification Scheme ◽

Analytical Procedure ◽

Narrow Band ◽

Scientific Research ◽

Maximum Amount ◽

Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

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Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010013064x ◽

1992 ◽

Vol 50 (2) ◽

pp. 1202-1203

Author(s):

Gianluigi Botton ◽

Gilles L'espérance

Keyword(s):

Statistical Analysis ◽

Spatial Resolution ◽

Personal Computer ◽

Systematic Study ◽

Present Author ◽

Chemical Elements ◽

Detection Limits ◽

Research Groups ◽

Quantitative Performance ◽

Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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