Related papers: A Flexible Parametrization of CKM matrix via Singu…
This paper explores signal and image analysis by using the Singular Value Decomposition (SVD) and its extension, the Generalized Singular Value Decomposition (GSVD). A key strength of SVD lies in its ability to separate information into…
We study flavor violation in the quark sector in a purely 4D, two-site effective field theory description of the Standard Model and just their first Kaluza-Klein excitations from a warped extra dimension. The warped 5D framework can provide…
In the light of several recent analyses pointing towards texture 4-zero Fritzsch-like quark mass matrices as the only viable structures for quark mass matrices, this work adopts a model independent approach to reconstruct an alternate and…
Combining the recently available experimental evidence of D-Dbar mixing, we extract model-independent information on the mixing amplitude and on its CP-violating phase. Using this information, we present new constraints on the flavour…
Variables in many massive high-dimensional data sets are structured, arising for example from measurements on a regular grid as in imaging and time series or from spatial-temporal measurements as in climate studies. Classical multivariate…
We consider an universal mass matrix model which has a seesaw-invariant structure with the most general texture based on flavor 2 (\leftrightarrow) 3 symmetry common to all quarks and leptons. The CKM quark mixing matrix of the model is…
The CKM matrix describing quark mixing with three generations can be parameterized by three mixing angles and one CP violating phase. In most of the parameterizations, the CP violating phase chosen is not a directly measurable quantity and…
We study the flavor physics in two extensions of the quark sector of the Standard Model (SM): a four generation model and a model with a single vector--like down--type quark (VDQ). In our analysis we take into account the experimental…
In this paper, we present a class of high order methods to approximate the singular value decomposition of a given complex matrix (SVD). To the best of our knowledge, only methods up to order three appear in the the literature. A first part…
We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (2019b) for exploratory Item Factor Analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for…
One flavor mass reweighting can be used in lattice QCD computations to fine tune the quark masses to their physical values. We present a new method that utilizes an unbiased stochastic estimation of the one flavor determinant. The…
We discuss our recently proposed S3(down)xS3(up) flavour-permutation-symmetric mixing observables, giving expressions for them in terms of (moduli-squared) of the mixing matrix elements. We outline their successful use in providing…
We constrain the flavor structure of Wilson coefficients in the Standard Model Effective Field Theory (SMEFT) from data. In the SMEFT, new physics effects in couplings of up-type and down-type quarks are related through the…
Modular transformations of string theory are shown to play a crucial role in the discussion of discrete flavor symmetries in the Standard Model. They include CP transformations and provide a unification of CP with traditional flavor…
In the scenario with four quark generations, we perform a fit using flavor-physics data and determine the allowed values -- preferred central values and errors -- of all of the elements of the 4X4 quark mixing matrix. In addition to the…
The Cabibbo-Kobayashi-Maskawa matrix is a key element to describe flavour dynamics in the Standard Model. With only four parameters, this matrix is able to describe a large range of phenomena in the quark sector, such as CP violation and…
In this paper a vectorized algorithm for simultaneously computing up to eight singular value decompositions (SVDs, each of the form $A=U\Sigma V^{\ast}$) of real or complex matrices of order two is proposed. The algorithm extends to a batch…
SVD (singular value decomposition) is one of the basic tools of machine learning, allowing to optimize basis for a given matrix. However, sometimes we have a set of matrices $\{A_k\}_k$ instead, and would like to optimize a single common…
The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank $k$ approximation of a matrix $A$ using matrix-vector products with standard Gaussian vectors. Here, we generalize the…
Assuming that the Kobayashi-Maskawa mechanism gives the dominant contribution to CP violation at low energies, we propose a novel way of testing the flavour sector of the Standard Model which has the potencial for discovering New Physics.…