91,667 research outputs found

    FastJet user manual

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    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

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    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

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    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

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    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

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    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

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    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

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    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 Microsoft’s 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

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    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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