Covariant gaussian approximation in Ginzburg–Landau model
نویسندگان
چکیده
منابع مشابه
Binary Image Registration Using Covariant Gaussian Densities
We consider the estimation of 2D affine transformations aligning a known binary shape and its distorted observation. The classical way to solve this registration problem is to find correspondences between the two images and then compute the transformation parameters from these landmarks. In this paper, we propose a novel approach where the exact transformation is obtained as a least-squares sol...
متن کاملLinear Σ Model in the Gaussian Functional Approximation
We apply a self-consistent relativistic mean-field variational “Gaussian functional” (or Hartree) approximation to the linear σ model with spontaneously and explicitly broken chiral O(4) symmetry. We set up the self-consistency, or “gap” and the Bethe-Salpeter equations. We check and confirm the chiral Ward-Takahashi identities, among them the Nambu-Goldstone theorem and the (partial) axial cur...
متن کاملA gauge covariant approximation to QED
We examine the fermion propagator in quenched QED in three and four dimension using gauge covariant approximation which preserves Ward-Takahashi Identity with appropriate renormalization to smooth the threshold behaviour of the fermion self energy .Thus we avoid the infrared singurality in three dimension. The behaviour of the fermion propagator in three dimension near the threshold is then fou...
متن کاملImproved Gaussian Approximation
In a recently developed approximation technique [1] for quantum field theory the standard one-loop result is used as a seed for a recursive formula that gives a sequence of improved Gaussian approximations for the generating functional. In this paper we work with the generic φ3+φ4 model in d = 0 dimensions. We compare the first, and simplest, approximation in the above sequence with the one-loo...
متن کاملModel Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel), which are determined by a model selection criterion. The functions to be compared do not just differ in their parametrization but in their fundamental structure. It is often not clear which function structure to ch...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annals of Physics
سال: 2017
ISSN: 0003-4916
DOI: 10.1016/j.aop.2017.03.015