23,310 research outputs found
Building a Sample of Distant Clusters of Galaxies
Candidate clusters of galaxies drawn from the sample identified from the
moderately deep I-band data of the ESO Imaging Survey (EIS), have been used for
follow-up optical/infrared imaging and spectroscopic observations. The
observations were conducted to assess the nature of these candidates over a
large range of redshifts. Currently, 163 EIS candidates have (V-I) colors, 15
have (I-K) and 65 cluster fields have been observed spectroscopically. From a
preliminary analysis of these data, we find that > 65% of the candidates
studied show strong evidence of being real physical associations, over the
redshift range 0.2<z<1.1. The evidence in some cases comes directly from
spectroscopic measurements, in others indirectly from the detection of
overdensities of objects with either the same color or the same photometric
redshift, or from a combination of color and spectroscopic information.
Preliminary results also suggest that the redshift derived from the
matched-filter algorithm is a reasonable measure of the cluster's redshift,
possibly overestimating it by Delta z ~0.1, at least for systems at z<0.7.
Overdensities of red objects have been detected in over 100 candidates, 38 of
which with estimated redshifts >0.6, and six candidates in the interval
0.45<z<0.81 have either been identified directly from measured redshifts or
have been confirmed by the measurement of at least one redshift for galaxies
located along a red-sequence typical of cluster early-type galaxies. Lastly,
five candidates among those already observed in the infrared have (I-Ks) colors
consistent with them being in the redshift interval 0.8<z<1.1. The sample of
"confirmed" clusters, already the largest of its kind in the southern
hemisphere, will be further enlarged by ongoing observations.Comment: To appear in "Large Scale Structure in the X-ray Universe", ed. M.
Plionis and I. Georgantopoulos (Paris: Editions Frontieres), in pres
Combinatorial formulation of Ising model revisited
In 1952, Kac and Ward developed a combinatorial formulation for the two
dimensional Ising model which is another method of obtaining Onsager's formula
for the free energy per site in the thermodynamic limit of the model. Feynman
gave an important contribution to this formulation conjecturing a crucial
mathematical relation which completed Kac and Ward ideas. In this paper, the
method of Kac, Ward and Feynman for the free field Ising model in two
dimensions is reviewed in a selfcontained way.Comment: 27 pages, 17 figure
Fast Community Identification by Hierarchical Growth
A new method for community identification is proposed which is founded on the
analysis of successive neighborhoods, reached through hierarchical growth from
a starting vertex, and on the definition of communities as a subgraph whose
number of inner connections is larger than outer connections. In order to
determine the precision and speed of the method, it is compared with one of the
most popular community identification approaches, namely Girvan and Newman's
algorithm. Although the hierarchical growth method is not as precise as Girvan
and Newman's method, it is potentially faster than most community finding
algorithms.Comment: 6 pages, 5 figure
Statistical Mechanics Characterization of Neuronal Mosaics
The spatial distribution of neuronal cells is an important requirement for
achieving proper neuronal function in several parts of the nervous system of
most animals. For instance, specific distribution of photoreceptors and related
neuronal cells, particularly the ganglion cells, in mammal's retina is required
in order to properly sample the projected scene. This work presents how two
concepts from the areas of statistical mechanics and complex systems, namely
the \emph{lacunarity} and the \emph{multiscale entropy} (i.e. the entropy
calculated over progressively diffused representations of the cell mosaic),
have allowed effective characterization of the spatial distribution of retinal
cells.Comment: 3 pages, 1 figure, The following article has been submitted to
Applied Physics Letters. If it is published, it will be found online at
http://apl.aip.org
Neuromorphometric characterization with shape functionals
This work presents a procedure to extract morphological information from
neuronal cells based on the variation of shape functionals as the cell geometry
undergoes a dilation through a wide interval of spatial scales. The targeted
shapes are alpha and beta cat retinal ganglion cells, which are characterized
by different ranges of dendritic field diameter. Image functionals are expected
to act as descriptors of the shape, gathering relevant geometric and
topological features of the complex cell form. We present a comparative study
of classification performance of additive shape descriptors, namely, Minkowski
functionals, and the nonadditive multiscale fractal. We found that the proposed
measures perform efficiently the task of identifying the two main classes alpha
and beta based solely on scale invariant information, while also providing
intraclass morphological assessment
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