64,041 research outputs found
Hadronic Molecular States Composed of Spin- Singly Charmed Baryons
We investigate the possible deuteron-like molecules composed of a pair of
charmed spin- baryons, or one charmed baryon and one charmed
antibaryon within the one-boson-exchange (OBE) model. For the spin singlet and
triplet systems, we consider the couple channel effect between systems with
different orbital angular momentum. Most of the systems have binding solutions.
The couple channel effect plays a significant role in the formation of some
loosely bound states. The possible molecular states of
and might be stable once produced.Comment: 18 pages, 7 figure
Exclusive Decays to Charmonium and a Light Meson at Next-to-Leading Order Accuracy
In this paper the next-to-leading order (NLO) corrections to meson
exclusive decays to S-wave charmonia and light pseudoscalar or vector mesons,
i.e. , , , and , are performed within non-relativistic (NR)
QCD approach. The non-factorizable contribution is included, which is absent in
traditional naive factorization (NF). And the theoretical uncertainties for
their branching ratios are reduced compared with that of direct tree level
calculation. Numerical results show that NLO QCD corrections markedly enhance
the branching ratio with a K factor of 1.75 for and 1.31 for . In order to
investigate the asymptotic behavior, the analytic form is obtained in the heavy
quark limit, i.e. . We note that annihilation topologies
contribute trivia in this limit, and the corrections at leading order in expansion come from form factors and hard spectator interactions. At
last, some related phenomenologies are also discussed.Comment: 20 pages, 7 figures and 5 table
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Nearest Neighbors (NN) is one of the most widely used supervised
learning algorithms to classify Gaussian distributed data, but it does not
achieve good results when it is applied to nonlinear manifold distributed data,
especially when a very limited amount of labeled samples are available. In this
paper, we propose a new graph-based NN algorithm which can effectively
handle both Gaussian distributed data and nonlinear manifold distributed data.
To achieve this goal, we first propose a constrained Tired Random Walk (TRW) by
constructing an -level nearest-neighbor strengthened tree over the graph,
and then compute a TRW matrix for similarity measurement purposes. After this,
the nearest neighbors are identified according to the TRW matrix and the class
label of a query point is determined by the sum of all the TRW weights of its
nearest neighbors. To deal with online situations, we also propose a new
algorithm to handle sequential samples based a local neighborhood
reconstruction. Comparison experiments are conducted on both synthetic data
sets and real-world data sets to demonstrate the validity of the proposed new
NN algorithm and its improvements to other version of NN algorithms.
Given the widespread appearance of manifold structures in real-world problems
and the popularity of the traditional NN algorithm, the proposed manifold
version NN shows promising potential for classifying manifold-distributed
data.Comment: 32 pages, 12 figures, 7 table
The next-to-next-to-leading order soft function for top quark pair production
We present the first calculation of the next-to-next-to-leading order
threshold soft function for top quark pair production at hadron colliders, with
full velocity dependence of the massive top quarks. Our results are fully
analytic, and can be entirely written in terms of generalized polylogarithms.
The scale-dependence of our result coincides with the well-known two-loop
anomalous dimension matrix including the three-parton correlations, which at
the two-loop order only appear when more than one massive partons are involved
in the scattering process. In the boosted limit, our result exhibits the
expected factorization property of mass logarithms, which leads to a consistent
extraction of the soft fragmentation function. The next-to-next-to-leading
order soft function obtained in this paper is an important ingredient for
threshold resummation at the next-to-next-to-next-to-leading logarithmic
accuracy.Comment: 34 pages, 9 figures; v2: added references, matches the published
versio
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