Neutrino Masses and Lepton Flavour Violation in Thick Brane Scenarios

We address the issue of lepton flavour violation and neutrino masses in the ``fat-brane'' paradigm, where flavour changing processes are suppressed by localising different fermion field wave-functions at different positions (in the extra dimensions) in a thick brane. We study the consequences of suppressing lepton number violating charged lepton decays within this scenario for lepton masses and mixing angles. In particular, we find that charged lepton mass matrices are constrained to be quasi-diagonal. We further consider whether the same paradigm can be used to naturally explain small Dirac neutrino masses by considering the existence of three right-handed neutrinos in the brane, and discuss the requirements to obtain phenomenologically viable neutrino masses and mixing angles. Finally, we examine models where neutrinos obtain a small Majorana mass by breaking lepton number in a far away brane and show that, if the fat-brane paradigm is the solution to the absence of lepton number violating charged lepton decays, such models predict, in the absence of flavour symmetries, that charged lepton flavour violation will be observed in the next round of rare muon/tau decay experiments.Comment: 33 pages, 9 eps figure

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided