7,899 research outputs found

    Distinguishing Dynamical Dark Matter at the LHC

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    Dynamical dark matter (DDM) is a new framework for dark-matter physics in which the dark sector comprises an ensemble of individual component fields which collectively conspire to act in ways that transcend those normally associated with dark matter. Because of its non-trivial structure, this DDM ensemble --- unlike most traditional dark-matter candidates --- cannot be characterized in terms of a single mass, decay width, or set of scattering cross-sections, but must instead be described by parameters which describe the collective behavior of its constituents. Likewise, the components of such an ensemble need not be stable so long as lifetimes are balanced against cosmological abundances across the ensemble as a whole. In this paper, we investigate the prospects for identifying a DDM ensemble at the LHC and for distinguishing such a dark-matter candidate from the candidates characteristic of traditional dark-matter models. In particular, we focus on DDM scenarios in which the component fields of the ensemble are produced at colliders alongside some number of Standard-Model particles via the decays of additional heavy fields. The invariant-mass distributions of these Standard-Model particles turn out to possess several unique features that cannot be replicated in most traditional dark-matter models. We demonstrate that in many situations it is possible to differentiate between a DDM ensemble and a traditional dark-matter candidate on the basis of such distributions. Moreover, many of our results also apply more generally to a variety of other extensions of the Standard Model which involve multiple stable or metastable neutral particles.Comment: 17 pages, LaTeX, 10 figure

    Observation of B0->D0K+K- and evidence for Bs0->D0K+K-

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    The first observation of the decay B0→D0K+K- is reported from an analysis of 0.62  fb-1 of pp collision data collected with the LHCb detector. Its branching fraction is measured relative to that of the topologically similar decay B0→D0π+π- to be B(B0→D0K+K-)/B(B0→D0π+π-)=0.056±0.011±0.007, where the first uncertainty is statistical and the second is systematic. The significance of the signal is 5.8σ. Evidence, with 3.8σ significance, for Bs0→D0K+K- decays is also presented. The relative branching fraction is measured to be B(Bs0→D0K+K-)/B(B0→D0K+K-)=0.90±0.27±0.20. These channels are of interest to study the mechanisms behind hadronic B decays, and open new possibilities for CP violation analyses with larger data sets

    Identifying Phase Space Boundaries with Voronoi Tessellations

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    Determining the masses of new physics particles appearing in decay chains is an important and longstanding problem in high energy phenomenology. Recently it has been shown that these mass measurements can be improved by utilizing the boundary of the allowed region in the fully differentiable phase space in its full dimensionality. Here we show that the practical challenge of identifying this boundary can be solved using techniques based on the geometric properties of the cells resulting from Voronoi tessellations of the relevant data. The robust detection of such phase space boundaries in the data could also be used to corroborate a new physics discovery based on a cut-and-count analysis.Comment: 48 pages, 23 figures, Journal-submitted versio

    Enhancing the discovery prospects for SUSY-like decays with a forgotten kinematic variable

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    The lack of a new physics signal thus far at the Large Hadron Collider motivates us to consider how to look for challenging final states, with large Standard Model backgrounds and subtle kinematic features, such as cascade decays with compressed spectra. Adopting a benchmark SUSY-like decay topology with a four-body final state proceeding through a sequence of two-body decays via intermediate resonances, we focus our attention on the kinematic variable Δ4\Delta_{4} which previously has been used to parameterize the boundary of the allowed four-body phase space. We highlight the advantages of using Δ4\Delta_{4} as a discovery variable, and present an analysis suggesting that the pairing of Δ4\Delta_{4} with another invariant mass variable leads to a significant improvement over more conventional variable choices and techniques.Comment: 20 pages, 13 figures. v2: matches published versio

    Massively Parallel Computing and the Search for Jets and Black Holes at the LHC

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    Massively parallel computing at the LHC could be the next leap necessary to reach an era of new discoveries at the LHC after the Higgs discovery. Scientific computing is a critical component of the LHC experiment, including operation, trigger, LHC computing GRID, simulation, and analysis. One way to improve the physics reach of the LHC is to take advantage of the flexibility of the trigger system by integrating coprocessors based on Graphics Processing Units (GPUs) or the Many Integrated Core (MIC) architecture into its server farm. This cutting edge technology provides not only the means to accelerate existing algorithms, but also the opportunity to develop new algorithms that select events in the trigger that previously would have evaded detection. In this article we describe new algorithms that would allow to select in the trigger new topological signatures that include non-prompt jet and black hole--like objects in the silicon tracker.Comment: 15 pages, 11 figures, submitted to NIM

    Automated Visual Fin Identification of Individual Great White Sharks

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    This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global `fin space'. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multi-instance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to update first author contact details and to correct a Figure reference on page

    Contour Detection from Deep Patch-level Boundary Prediction

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    In this paper, we present a novel approach for contour detection with Convolutional Neural Networks. A multi-scale CNN learning framework is designed to automatically learn the most relevant features for contour patch detection. Our method uses patch-level measurements to create contour maps with overlapping patches. We show the proposed CNN is able to to detect large-scale contours in an image efficienly. We further propose a guided filtering method to refine the contour maps produced from large-scale contours. Experimental results on the major contour benchmark databases demonstrate the effectiveness of the proposed technique. We show our method can achieve good detection of both fine-scale and large-scale contours.Comment: IEEE International Conference on Signal and Image Processing 201
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