R-matrix and K-matrix analysis of elastic α-α scattering
نویسندگان
چکیده
منابع مشابه
Further Investigation of α+12C and α+16O Elastic Scattering
Abstract—The current work aims to study the rainbow likestructure observed in the elastic scattering of alpha particles on both 12C and 16O nuclei. We reanalyzed the experimental elastic scattering angular distributions data for α+12C and α+16O nuclear systems at different energies using both optical model and double folding potential of different interaction models such as: CDM3Y1, DDM3Y1, CDM...
متن کاملO ( α , γ ) 20 Ne S factor : Measurements and R - matrix analysis
H. Costantini,1,2 R. J. deBoer,2,* R. E. Azuma,2,3 M. Couder,2 J. Görres,2 J. W. Hammer,2,† P. J. LeBlanc,2 H. Y. Lee,4 S. O’Brien,2 A. Palumbo,2 E. C. Simpson,5 E. Stech,2 W. Tan,2 E. Uberseder,2 and M. Wiescher2 1Instituto Nazionale di Fisica Nucleare, Sezione di Genova, Genova, Italy 2Department of Physics, University of Notre Dame, Notre Dame, Indiana 46556, USA 3Department of Physics, Univ...
متن کاملMatrix theory of elastic wave scattering
Upon invoking Huygen's principle, matrix equations are obtained describing the scattering of waves by an obstacle of arbitrary shape immersed in an elastic medium. New relations are found connecting surface tractions with the divergence and curl of the displacement, and conservation laws are discussed. When mode conversion effects are arbitrarily suppressed by resetting appropriate matrix eleme...
متن کاملNonnegative matrix factorization with α-divergence
Nonnegative matrix factorization (NMF) is a popular technique for pattern recognition, data analysis, and dimensionality reduction, the goal of which is to decompose nonnegative data matrix X into a product of basis matrix A and encoding variable matrix S with both A and S allowed to have only nonnegative elements. In this paper we consider Amari’s α-divergence as a discrepancy measure and rigo...
متن کاملProjective Nonnegative Matrix Factorization with α-Divergence
A new matrix factorization algorithm which combines two recently proposed nonnegative learning techniques is presented. Our new algorithm, α-PNMF, inherits the advantages of Projective Nonnegative Matrix Factorization (PNMF) for learning a highly orthogonal factor matrix. When the Kullback-Leibler (KL) divergence is generalized to αdivergence, it gives our method more flexibility in approximati...
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: Nuclear Physics A
سال: 1998
ISSN: 0375-9474
DOI: 10.1016/s0375-9474(98)00419-9