Pati-Salam unification from noncommutative geometry and the TeV-scale W-R boson

Download
2016-01-01
Aydemir, Ufuk
Minic, Djordje
Sun, Chen
Takeuchi, Tatsu
We analyze the compatibility of the unified left-right symmetric Pati-Salam models motivated by noncommutative geometry and the TeV-scale right-handed W boson suggested by recent LHC data. We find that the unification/matching conditions place conflicting demands on the symmetry breaking scales and that generating the required W-R mass and coupling is nontrivial.
INTERNATIONAL JOURNAL OF MODERN PHYSICS A

Suggestions

SO(10) grand unification in light of recent LHC searches and colored scalars at the TeV-scale
Aydemir, Ufuk (2016-03-01)
e analyze the compatibility of the recent LHC signals and the TeV-scale left right model(s) in the minimal nonsupersymmetric SO(10) framework. We show that the models in which the Higgs content is selected based on the extended survival hypothesis do not allow the W-R boson to be at the TeV-scale. By relaxing this conjecture, we investigate various scenarios where a number of colored-scalars, originated from various Pati-Salam multiplets, are light and whence they survive down to the low energies. Performin...
Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks
Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Weber, Gerhard Wilhelm (2018-11-01)
The Gaussian Graphical Model (GGM) and its Bayesian alternative, called, the Gaussian copula graphical model (GCGM) are two widely used approaches to construct the undirected networks of biological systems. They define the interactions between species by using the conditional dependencies of the multivariate normality assumption. However, when the system's dimension is high, the performance of the model becomes computationally demanding, and, particularly, the accuracy of GGM decreases when the observations...
Hybrid wavelet-neural network models for time series data
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3)
The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
Higher order approximate dynamic models for layered composites
Yalcin, Omer Fatih; Mengi, Yalcin; Turhan, Dogan (Elsevier BV, 2007-05-01)
Based on a higher order dynamic approximate theory developed in the present study for anisotropic elastic plates, two dynamic models, discrete and continuum models (DM and CM), are proposed for layered composites. Of the two models, CM is more important, which is established in the study of periodic layered composites using smoothing operations. CM has the properties: it contains inherently the interface and Floquet conditions and facilitates the analysis of the. composite, in particular, when the number of...
Hilbert functions of gorenstein monomial curves
Topaloğlu Mete, Pınar; Arslan, Sefa Feza; Department of Mathematics (2005)
The aim of this thesis is to study the Hilbert function of a one-dimensional Gorenstein local ring of embedding dimension four in the case of monomial curves. We show that the Hilbert function is non-decreasing for some families of Gorenstein monomial curves in affine 4-space. In order to prove this result, under some arithmetic assumptions on generators of the defining ideal, we determine the minimal generators of their tangent cones by using the standard basis and check the Cohen-Macaulayness of them. Lat...
Citation Formats
U. Aydemir, D. Minic, C. Sun, and T. Takeuchi, “Pati-Salam unification from noncommutative geometry and the TeV-scale W-R boson,” INTERNATIONAL JOURNAL OF MODERN PHYSICS A, vol. 31, no. 1, pp. 0–0, 2016, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/101034.