54,693 research outputs found
Beyond the Standard Model with Precision Nucleon Matrix Elements on the Lattice
Precision measurements of nucleons provide constraints on the Standard Model
and can discern the signatures predicted for particles beyond the Standard
Model (BSM). Knowing the Standard Model inputs to nucleon matrix elements will
be necessary to constrain the couplings of dark matter candidates such as the
neutralino, to relate the neutron electric dipole moment to the CP-violating
theta parameter, or to search for new TeV-scale particles though non-
interactions in neutron beta decay. However, these matrix elements derive from
the properties of quantum chromodynamics (QCD) at low energies, where
perturbative treatments fail. Using lattice gauge theory, we can
nonperturbatively calculate the QCD path integral on a supercomputer. In this
proceeding, I will discuss a few representative areas in which lattice QCD
(LQCD) can contribute to the search for BSM physics, emphasizing suppressed
operators in neutron decay, and outline prospects for future development.Comment: 3 pages, 2 figures, talk presented at the 19th Particles and Nuclei
International Conference (PANIC 2011), Massachusetts Institute of Technology,
Cambridge, MA, USA, July 24-29, 201
A CASE STUDY ON SUPPORT VECTOR MACHINES VERSUS ARTIFICIAL NEURAL NETWORKS
The capability of artificial neural networks for pattern recognition of real world problems is well known. In recent years, the support vector machine has been advocated for its structure risk minimization leading to tolerance margins of decision boundaries. Structures and performances of these pattern classifiers depend on the feature dimension and training data size. The objective of this research is to compare these pattern recognition systems based on a case study. The particular case considered is on classification of hypertensive and normotensive right ventricle (RV) shapes obtained from Magnetic Resonance Image (MRI) sequences. In this case, the feature dimension is reasonable, but the available training data set is small, however, the decision surface is highly nonlinear.For diagnosis of congenital heart defects, especially those associated with pressure and volume overload problems, a reliable pattern classifier for determining right ventricle function is needed. RV¡¦s global and regional surface to volume ratios are assessed from an individual¡¦s MRI heart images. These are used as features for pattern classifiers. We considered first two linear classification methods: the Fisher linear discriminant and the linear classifier trained by the Ho-Kayshap algorithm. When the data are not linearly separable, artificial neural networks with back-propagation training and radial basis function networks were then considered, providing nonlinear decision surfaces. Thirdly, a support vector machine was trained which gives tolerance margins on both sides of the decision surface. We have found in this case study that the back-propagation training of an artificial neural network depends heavily on the selection of initial weights, even though randomized. The support vector machine where radial basis function kernels are used is easily trained and provides decision tolerance margins, in spite of only small margins
Charmed spectroscopy from a nonperturbatively determined relativistic heavy quark action in full QCD
We present a preliminary calculation of the charmed meson spectrum using the
2+1 flavor domain wall fermion lattice configurations currently being generated
by the RBC and UKQCD collaborations. The calculation is performed using the
3-parameter, relativistic heavy quark action with nonperturbatively determined
coefficients. We will also demonstrate a step-scaling procedure for determining
these coefficients nonperturbatively using a series of quenched, gauge field
ensembles generated for three different lattice spacings.Comment: Proceeding for 24th International Symposium on Lattice Field Theory
(Lattice 2006), Tucson, Arizona, 23-28 Jul 200
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