34,449 research outputs found
Chiral-Odd and Spin-Dependent Quark Fragmentation Functions and their Applications
We define a number of quark fragmentation functions for spin-0, -1/2 and -1
hadrons, and classify them according to their twist, spin and chirality. As an
example of their applications, we use them to analyze semi-inclusive
deep-inelastic scattering on a transversely polarized nucleon.Comment: 19 pages in Plain TeX, MIT CTP #221
Counting Form Factors of Twist-Two Operators
We present a simple method to count the number of hadronic form factors based
on the partial wave formalism and crossing symmetry. In particular, we show
that the number of independent nucleon form factors of spin-n, twist-2
operators (the vector current and energy-momentum tensor being special
examples) is n+1. These generalized form factors define the generalized
(off-forward) parton distributions that have been studied extensively in the
recent literature. In proving this result, we also show how the J^{PC} rules
for onium states arise in the helicity formalism.Comment: 7 pages, LaTeX (revtex
Leading Chiral Contributions to the Spin Structure of the Proton
The leading chiral contributions to the quark and gluon components of the
proton spin are calculated using heavy-baryon chiral perturbation theory.
Similar calculations are done for the moments of the generalized parton
distributions relevant to the quark and gluon angular momentum densities. These
results provide useful insight about the role of pions in the spin structure of
the nucleon, and can serve as a guidance for extrapolating lattice QCD
calculations at large quark masses to the chiral limit.Comment: 8 pages, 2 figures; a typo in Ref. 7 correcte
Accelerating Stochastic Composition Optimization
Consider the stochastic composition optimization problem where the objective
is a composition of two expected-value functions. We propose a new stochastic
first-order method, namely the accelerated stochastic compositional proximal
gradient (ASC-PG) method, which updates based on queries to the sampling oracle
using two different timescales. The ASC-PG is the first proximal gradient
method for the stochastic composition problem that can deal with nonsmooth
regularization penalty. We show that the ASC-PG exhibits faster convergence
than the best known algorithms, and that it achieves the optimal sample-error
complexity in several important special cases. We further demonstrate the
application of ASC-PG to reinforcement learning and conduct numerical
experiments
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