9,010 research outputs found
Stop Decay with LSP Gravitino in the final state:
In MSSM scenarios where the gravitino is the lightest supersymmetric particle
(LSP), and therefore a viable dark matter candidate, the stop
could be the next-to-lightest superpartner (NLSP). For a mass spectrum
satisfying: ,
the stop decay is dominated by the 3-body mode . We calculate the stop life-time, including the full
contributions from top, sbottom and chargino as intermediate states. We also
evaluate the stop lifetime for the case when the gravitino can be approximated
by the goldstino state. Our analytical results are conveniently expressed using
an expansion in terms of the intermediate state mass, which helps to identify
the massless limit.
In the region of low gravitino mass ()
the results obtained using the gravitino and goldstino cases turns out to be
similar, as expected. However for higher gravitino masses the results for the lifetime could show a difference
of O(100)\%
Relevance of the purity level in a MetalOrganic Vapour Phase Epitaxy reactor environment for the growth of high quality pyramidal sitecontrolled Quantum Dots
We report in this work on the spectral purity of pyramidal site-controlled
InGaAs/AlGaAs Quantum Dots grown by metalorganic vapour phase epitaxy on(111)B
oriented GaAs substrates. Extremely sharp emission peaks were found, showing
linewidths surprisingly narrow (~27{\mu}eV) and comparable to those which can
be obtained by Molecular Beam Epitaxy in an ultra-high vacuum environment. A
careful reactor handling is regarded as a crucial step toward the fabrication
of high optical quality systems.Comment: ICMOVPE 2010 Proceedin
Exponential Runge-Kutta methods for stiff kinetic equations
We introduce a class of exponential Runge-Kutta integration methods for
kinetic equations. The methods are based on a decomposition of the collision
operator into an equilibrium and a non equilibrium part and are exact for
relaxation operators of BGK type. For Boltzmann type kinetic equations they
work uniformly for a wide range of relaxation times and avoid the solution of
nonlinear systems of equations even in stiff regimes. We give sufficient
conditions in order that such methods are unconditionally asymptotically stable
and asymptotic preserving. Such stability properties are essential to guarantee
the correct asymptotic behavior for small relaxation times. The methods also
offer favorable properties such as nonnegativity of the solution and entropy
inequality. For this reason, as we will show, the methods are suitable both for
deterministic as well as probabilistic numerical techniques
Single-stage electrohydraulic servosystem for actuating on airflow valve with frequencies to 500 hertz
An airflow valve and its electrohydraulic actuation servosystem are described. The servosystem uses a high-power, single-stage servovalve to obtain a dynamic response beyond that of systems designed with conventional two-stage servovalves. The electrohydraulic servosystem is analyzed and the limitations imposed on system performance by such nonlinearities as signal saturations and power limitations are discussed. Descriptions of the mechanical design concepts and developmental considerations are included. Dynamic data, in the form of sweep-frequency test results, are presented and comparison with analytical results obtained with an analog computer model is made
Semi-Supervised Deep Learning for Fully Convolutional Networks
Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studied for standard CNN architectures. However, Fully
Convolutional Networks (FCNs) set the state-of-the-art for many image
segmentation tasks. To the best of our knowledge, there is no existing
semi-supervised learning method for such FCNs yet. We lift the concept of
auxiliary manifold embedding for semi-supervised learning to FCNs with the help
of Random Feature Embedding. In our experiments on the challenging task of MS
Lesion Segmentation, we leverage the proposed framework for the purpose of
domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure
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