26,836 research outputs found
Significant effects of weak gravitational lensing on determinations of the cosmology from Type Ia Supernov\ae
Significant adjustments to the values of the cosmological parameters
estimated from high-redshift Type Ia Supernov\ae data are reported, almost an
order of magnitude greater than previously found. They arise from the effects
of weak gravitational lensing on observations of high-redshift sources. The
lensing statistics used have been obtained from computations of the
three-dimensional shear in a range of cosmological N-body simulations, from
which it is estimated that cosmologies with an underlying deceleration
parameter q_0 = -0.51 +0.03/-0.24 may be interpreted as having q_0 = -0.55
(appropriate to the currently popular cosmology with density parameter
and vacuum energy density parameter ).
In addition, the standard deviation expected from weak lensing for the peak
magnitudes of Type Ia Supernov\ae at redshifts of 1 is expected to be
approximately 0.078 magnitudes, and 0.185 magnitudes at redshift 2. This latter
value is greater than the accepted intrinsic dispersion of 0.17 magnitudes.
Consequently, the effects of weak lensing in observations of high-redshift
sources must be taken properly into account.Comment: 9 pages, LaTeX, 4 figure
Detecting hierarchical and overlapping network communities using locally optimal modularity changes
Agglomerative clustering is a well established strategy for identifying
communities in networks. Communities are successively merged into larger
communities, coarsening a network of actors into a more manageable network of
communities. The order in which merges should occur is not in general clear,
necessitating heuristics for selecting pairs of communities to merge. We
describe a hierarchical clustering algorithm based on a local optimality
property. For each edge in the network, we associate the modularity change for
merging the communities it links. For each community vertex, we call the
preferred edge that edge for which the modularity change is maximal. When an
edge is preferred by both vertices that it links, it appears to be the optimal
choice from the local viewpoint. We use the locally optimal edges to define the
algorithm: simultaneously merge all pairs of communities that are connected by
locally optimal edges that would increase the modularity, redetermining the
locally optimal edges after each step and continuing so long as the modularity
can be further increased. We apply the algorithm to model and empirical
networks, demonstrating that it can efficiently produce high-quality community
solutions. We relate the performance and implementation details to the
structure of the resulting community hierarchies. We additionally consider a
complementary local clustering algorithm, describing how to identify
overlapping communities based on the local optimality condition.Comment: 10 pages; 4 tables, 3 figure
Cryogenic insulation technology review for the space shuttle
Cryogenic insulation systems for space shuttl
NetzCope: A Tool for Displaying and Analyzing Complex Networks
Networks are a natural and popular mechanism for the representation and
investigation of a broad class of systems. But extracting information from a
network can present significant challenges. We present NetzCope, a software
application for the display and analysis of networks. Its key features include
the visualization of networks in two or three dimensions, the organization of
vertices to reveal structural similarity, and the detection and visualization
of network communities by modularity maximization.Comment: 16 pages, Proceedings of ICQBIC2010; minor improvements to wording in
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