7,899 research outputs found
Distinguishing Dynamical Dark Matter at the LHC
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-
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
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
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
which previously has been used to parameterize the boundary of the
allowed four-body phase space. We highlight the advantages of using
as a discovery variable, and present an analysis suggesting that
the pairing of 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
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
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
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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