32,195 research outputs found
A Simple Method for Computing the Non-Linear Mass Correlation Function with Implications for Stable Clustering
We propose a simple and accurate method for computing analytically the mass
correlation function for cold dark matter and scale-free models that fits
N-body simulations over a range that extends from the linear to the strongly
non-linear regime. The method, based on the dynamical evolution of the pair
conservation equation, relies on a universal relation between the pair-wise
velocity and the smoothed correlation function valid for high and low density
models, as derived empirically from N-body simulations. An intriguing
alternative relation, based on the stable-clustering hypothesis, predicts a
power-law behavior of the mass correlation function that disagrees with N-body
simulations but conforms well to the observed galaxy correlation function if
negligible bias is assumed. The method is a useful tool for rapidly exploring a
wide span of models and, at the same time, raises new questions about large
scale structure formation.Comment: 10 pages, 3 figure
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy
The relation between the two-point and the three-point correlation functions in the non-linear gravitational clustering regime
The connection between the two-point and the three-point correlation
functions in the non-linear gravitational clustering regime is studied. Under a
scaling hypothesis, we find that the three-point correlation function, ,
obeys the scaling law in the
nonlinear regime, where , , , and are the two-point
correlation function, the power index of the power spectrum in the nonlinear
regime, the number of spatial dimensions, and the power index of the phase
correlations, respectively. The new formula reveals the origin of the power
index of the three-point correlation function. We also obtain the theoretical
condition for which the ``hierarchical form'' is
reproduced.Comment: 16 pages, 4 figures. Accepted for publication in APJ. Some sentences
and figures are revise
Evolutionary dynamics on strongly correlated fitness landscapes
We study the evolutionary dynamics of a maladapted population of
self-replicating sequences on strongly correlated fitness landscapes. Each
sequence is assumed to be composed of blocks of equal length and its fitness is
given by a linear combination of four independent block fitnesses. A mutation
affects the fitness contribution of a single block leaving the other blocks
unchanged and hence inducing correlations between the parent and mutant
fitness. On such strongly correlated fitness landscapes, we calculate the
dynamical properties like the number of jumps in the most populated sequence
and the temporal distribution of the last jump which is shown to exhibit a
inverse square dependence as in evolution on uncorrelated fitness landscapes.
We also obtain exact results for the distribution of records and extremes for
correlated random variables
Persistence in the Zero-Temperature Dynamics of the Diluted Ising Ferromagnet in Two Dimensions
The non-equilibrium dynamics of the strongly diluted random-bond Ising model
in two-dimensions (2d) is investigated numerically.
The persistence probability, P(t), of spins which do not flip by time t is
found to decay to a non-zero, dilution-dependent, value . We find
that decays exponentially to zero at large times.
Furthermore, the fraction of spins which never flip is a monotonically
increasing function over the range of bond-dilution considered. Our findings,
which are consistent with a recent result of Newman and Stein, suggest that
persistence in disordered and pure systems falls into different classes.
Furthermore, its behaviour would also appear to depend crucially on the
strength of the dilution present.Comment: some minor changes to the text, one additional referenc
Hierarchy in the Phase Space and Dark Matter Astronomy
We develop a theoretical framework for describing the hierarchical structure
of the phase space of cold dark matter haloes, due to gravitationally bound
substructures. Because it includes the full hierarchy of the cold dark matter
initial conditions and is hence complementary to the halo model, the stable
clustering hypothesis is applied for the first time here to the small-scale
phase space structure. As an application, we show that the particle dark matter
annihilation signal could be up to two orders of magnitude larger than that of
the smooth halo within the Galactic virial radius. The local boost is inversely
proportional to the smooth halo density, and thus is O(1) within the solar
radius, which could translate into interesting signatures for dark matter
direct detection experiments: The temporal correlation of dark matter detection
can change by a factor of 2 in the span of 10 years, while there will be
significant correlations in the velocity space of dark matter particles. This
can introduce O(1) uncertainty in the direction of local dark matter wind,
which was believed to be a benchmark of directional dark matter searches or the
annual modulation signal.Comment: 5 pages, 4 figure
Cumulative effect of Forbush decreases in the heliospheric modulation during the present solar cycle
A monthly Forbush decrease index (Fd-I) is generated and it is compared with the observed long term chnges in the cosmic ray intensity near earth at energies greater than or equal to 1 Gev over 1976-83. Significant correlation is observed between the two except for 1978. Such an effect is also seen in the correlation plot between the solar flare index (SFI) and Fd-I
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