12,253 research outputs found
Improved Orientation Sampling for Indexing Diffraction Patterns of Polycrystalline Materials
Orientation mapping is a widely used technique for revealing the
microstructure of a polycrystalline sample. The crystalline orientation at each
point in the sample is determined by analysis of the diffraction pattern, a
process known as pattern indexing. A recent development in pattern indexing is
the use of a brute-force approach, whereby diffraction patterns are simulated
for a large number of crystalline orientations, and compared against the
experimentally observed diffraction pattern in order to determine the most
likely orientation. Whilst this method can robust identify orientations in the
presence of noise, it has very high computational requirements. In this
article, the computational burden is reduced by developing a method for
nearly-optimal sampling of orientations. By using the quaternion representation
of orientations, it is shown that the optimal sampling problem is equivalent to
that of optimally distributing points on a four-dimensional sphere. In doing
so, the number of orientation samples needed to achieve a indexing desired
accuracy is significantly reduced. Orientation sets at a range of sizes are
generated in this way for all Laue groups, and are made available online for
easy use.Comment: 11 pages, 7 figure
Betti number signatures of homogeneous Poisson point processes
The Betti numbers are fundamental topological quantities that describe the
k-dimensional connectivity of an object: B_0 is the number of connected
components and B_k effectively counts the number of k-dimensional holes.
Although they are appealing natural descriptors of shape, the higher-order
Betti numbers are more difficult to compute than other measures and so have not
previously been studied per se in the context of stochastic geometry or
statistical physics.
As a mathematically tractable model, we consider the expected Betti numbers
per unit volume of Poisson-centred spheres with radius alpha. We present
results from simulations and derive analytic expressions for the low intensity,
small radius limits of Betti numbers in one, two, and three dimensions. The
algorithms and analysis depend on alpha-shapes, a construction from
computational geometry that deserves to be more widely known in the physics
community.Comment: Submitted to PRE. 11 pages, 10 figure
A method for dense packing discovery
The problem of packing a system of particles as densely as possible is
foundational in the field of discrete geometry and is a powerful model in the
material and biological sciences. As packing problems retreat from the reach of
solution by analytic constructions, the importance of an efficient numerical
method for conducting \textit{de novo} (from-scratch) searches for dense
packings becomes crucial. In this paper, we use the \textit{divide and concur}
framework to develop a general search method for the solution of periodic
constraint problems, and we apply it to the discovery of dense periodic
packings. An important feature of the method is the integration of the unit
cell parameters with the other packing variables in the definition of the
configuration space. The method we present led to improvements in the
densest-known tetrahedron packing which are reported in [arXiv:0910.5226].
Here, we use the method to reproduce the densest known lattice sphere packings
and the best known lattice kissing arrangements in up to 14 and 11 dimensions
respectively (the first such numerical evidence for their optimality in some of
these dimensions). For non-spherical particles, we report a new dense packing
of regular four-dimensional simplices with density
and with a similar structure to the densest known tetrahedron packing.Comment: 15 pages, 5 figure
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