91,667 research outputs found
FastJet user manual
FastJet is a C++ package that provides a broad range of jet finding and
analysis tools. It includes efficient native implementations of all widely used
2-to-1 sequential recombination jet algorithms for pp and e+e- collisions, as
well as access to 3rd party jet algorithms through a plugin mechanism,
including all currently used cone algorithms. FastJet also provides means to
facilitate the manipulation of jet substructure, including some common boosted
heavy-object taggers, as well as tools for estimation of pileup and
underlying-event noise levels, determination of jet areas and subtraction or
suppression of noise in jets.Comment: 69 pages. FastJet 3 is available from http://fastjet.fr
Image Segmentation with Eigenfunctions of an Anisotropic Diffusion Operator
We propose the eigenvalue problem of an anisotropic diffusion operator for
image segmentation. The diffusion matrix is defined based on the input image.
The eigenfunctions and the projection of the input image in some eigenspace
capture key features of the input image. An important property of the model is
that for many input images, the first few eigenfunctions are close to being
piecewise constant, which makes them useful as the basis for a variety of
applications such as image segmentation and edge detection. The eigenvalue
problem is shown to be related to the algebraic eigenvalue problems resulting
from several commonly used discrete spectral clustering models. The relation
provides a better understanding and helps developing more efficient numerical
implementation and rigorous numerical analysis for discrete spectral
segmentation methods. The new continuous model is also different from
energy-minimization methods such as geodesic active contour in that no initial
guess is required for in the current model. The multi-scale feature is a
natural consequence of the anisotropic diffusion operator so there is no need
to solve the eigenvalue problem at multiple levels. A numerical implementation
based on a finite element method with an anisotropic mesh adaptation strategy
is presented. It is shown that the numerical scheme gives much more accurate
results on eigenfunctions than uniform meshes. Several interesting features of
the model are examined in numerical examples and possible applications are
discussed
Towards an Efficient Discovery of the Topological Representative Subgraphs
With the emergence of graph databases, the task of frequent subgraph
discovery has been extensively addressed. Although the proposed approaches in
the literature have made this task feasible, the number of discovered frequent
subgraphs is still very high to be efficiently used in any further exploration.
Feature selection for graph data is a way to reduce the high number of frequent
subgraphs based on exact or approximate structural similarity. However, current
structural similarity strategies are not efficient enough in many real-world
applications, besides, the combinatorial nature of graphs makes it
computationally very costly. In order to select a smaller yet structurally
irredundant set of subgraphs, we propose a novel approach that mines the top-k
topological representative subgraphs among the frequent ones. Our approach
allows detecting hidden structural similarities that existing approaches are
unable to detect such as the density or the diameter of the subgraph. In
addition, it can be easily extended using any user defined structural or
topological attributes depending on the sought properties. Empirical studies on
real and synthetic graph datasets show that our approach is fast and scalable
Spherical collapse in quintessence models with zero speed of sound
We study the spherical collapse model in the presence of quintessence with
negligible speed of sound. This case is particularly motivated for w<-1 as it
is required by stability. As pressure gradients are negligible, quintessence
follows dark matter during the collapse. The spherical overdensity behaves as a
separate closed FLRW universe, so that its evolution can be studied exactly. We
derive the critical overdensity for collapse and we use the extended
Press-Schechter theory to study how the clustering of quintessence affects the
dark matter mass function. The effect is dominated by the modification of the
linear dark matter growth function. A larger effect occurs on the total mass
function, which includes the quintessence overdensities. Indeed, here
quintessence constitutes a third component of virialized objects, together with
baryons and dark matter, and contributes to the total halo mass by a fraction ~
(1+w) Omega_Q / Omega_m. This gives a distinctive modification of the total
mass function at low redshift.Comment: 38 pages; small changes, including modification of the window
function. JCAP published versio
Spatial Correlation Function of X-ray Selected AGN
We present a detailed description of the first direct measurement of the
spatial correlation function of X-ray selected AGN. This result is based on an
X-ray flux-limited sample of 219 AGN discovered in the contiguous 80.7 deg^2
region of the ROSAT North Ecliptic Pole (NEP) Survey. Clustering is detected at
the 4 sigma level at comoving scales in the interval r = 5-60 h^-1 Mpc. Fitting
the data with a power law of slope gamma=1.8, we find a correlation length of
r_0 = 7.4 (+1.8, -1.9) h^-1 Mpc (Omega_M=0.3, Omega_Lambda=0.7). The median
redshift of the AGN contributing to the signal is z_xi=0.22. This clustering
amplitude implies that X-ray selected AGN are spatially distributed in a manner
similar to that of optically selected AGN. Furthermore, the ROSAT NEP
determination establishes the local behavior of AGN clustering, a regime which
is poorly sampled in general. Combined with high-redshift measures from optical
studies, the ROSAT NEP results argue that the AGN correlation strength
essentially does not evolve with redshift, at least out to z~2.2. In the local
Universe, X-ray selected AGN appear to be unbiased relative to galaxies and the
inferred X-ray bias parameter is near unity, b_X~1. Hence X-ray selected AGN
closely trace the underlying mass distribution. The ROSAT NEP AGN catalog,
presented here, features complete optical identifications and spectroscopic
redshifts. The median redshift, X-ray flux, and X-ray luminosity are z=0.41,
f_X=1.1*10^-13 cgs, and L_X=9.2*10^43 h_70^-2 cgs (0.5-2.0 keV), respectively.
Unobscured, type 1 AGN are the dominant constituents (90%) of this soft X-ray
selected sample of AGN.Comment: 17 pages, 8 figures, accepted for publication in ApJ, a version with
high-resolution figures is available at
http://www.eso.org/~cmullis/papers/Mullis_et_al_2004b.ps.gz, a
machine-readable version of the ROSAT NEP AGN catalog is available at
http://www.eso.org/~cmullis/research/nep-catalog.htm
Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science
The purpose of the New York Workshop on Computer, Earth and Space Sciences is
to bring together the New York area's finest Astronomers, Statisticians,
Computer Scientists, Space and Earth Scientists to explore potential synergies
between their respective fields. The 2011 edition (CESS2011) was a great
success, and we would like to thank all of the presenters and participants for
attending. This year was also special as it included authors from the upcoming
book titled "Advances in Machine Learning and Data Mining for Astronomy". Over
two days, the latest advanced techniques used to analyze the vast amounts of
information now available for the understanding of our universe and our planet
were presented. These proceedings attempt to provide a small window into what
the current state of research is in this vast interdisciplinary field and we'd
like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011
in New York City, Goddard Institute for Space Studie
Exploiting context information to aid landmark detection in SenseCam images
In this paper, we describe an approach designed to exploit
context information in order to aid the detection of landmark images from a large collection of photographs. The
photographs were generated using Microsoftâs SenseCam, a
device designed to passively record a visual diary and cover
a typical day of the user wearing the camera. The proliferation of digital photos along with the associated problems of managing and organising these collections provide the background motivation for this work. We believe more ubiquitious cameras, such as SenseCam, will become the norm in the future and the management of the volume of data generated by such devices is a key issue. The goal of the work reported here is to use context information to assist in the detection of landmark images or sequences of images from the thousands of photos taken daily by SenseCam. We will achieve this by analysing the images using low-level MPEG-7 features along with metadata provided by SenseCam, followed by simple clustering to identify the landmark images
Algorithms of maximum likelihood data clustering with applications
We address the problem of data clustering by introducing an unsupervised,
parameter free approach based on maximum likelihood principle. Starting from
the observation that data sets belonging to the same cluster share a common
information, we construct an expression for the likelihood of any possible
cluster structure. The likelihood in turn depends only on the Pearson's
coefficient of the data. We discuss clustering algorithms that provide a fast
and reliable approximation to maximum likelihood configurations. Compared to
standard clustering methods, our approach has the advantages that i) it is
parameter free, ii) the number of clusters need not be fixed in advance and
iii) the interpretation of the results is transparent. In order to test our
approach and compare it with standard clustering algorithms, we analyze two
very different data sets: Time series of financial market returns and gene
expression data. We find that different maximization algorithms produce similar
cluster structures whereas the outcome of standard algorithms has a much wider
variability.Comment: Accepted by Physica A; 12 pag., 5 figures. More information at:
http://www.sissa.it/dataclusterin
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