Properties from random matrix theory allow us to uncover naturally embedded signals from different data sets. While there are many parameters that can be changed, including the probability ...

Random Matrix Theory (RMT) has emerged as a potent framework to characterise the statistical properties of eigenvalues in large complex systems, bridging disciplines from quantum physics to number ...

In December 2019, the COVID-19 pandemic began, which has claimed the lives of millions of people around the world. This article presents a regional analysis of COVID-19 in Mexico. Due to comorbidities ...

Probability distributions of different neutrino mass models. The horizontal axis shows the ordinary logarithm of the neutrino mass squared difference ratio, while the vertical axis shows their ...

We place ourselves in the setting of high-dimensional statistical inference where the number of variables p in a dataset of interest is of the same order of magnitude as the number of observations n.

Scientists have used random matrix theory to theoretically show that neutrino mass hierarchy can be mathematically explained. Random matrix theory helps explain neutrino mass differences, supporting ...

Scientists analyzed each element of the neutrino mass matrix belonging to leptons and showed theoretically that the intergenerational mixing of lepton flavors is large. Furthermore, by using the ...

Python scripts from paper Optimal cleaning for singular values of cross-covariance matrices, by Florent Benaych-Georges, Jean-Philippe Bouchaud, Marc Potters (see ...

This code reproduces the data used in Random matrix theory of polarized light scattering in disordered media, N. Byrnes and M.R. Foreman, 2022. This code requires the ...