Muon capture in nuclei: an ab initio approach based on quantum Monte Carlo methods

An ab initio quantum Monte Carlo method is introduced for calculating total rates of muon weak capture in light nuclei with mass number $A \leq 12$. As a first application of the method, we perform a calculation of the rate in $^4$He in a dynamical framework based on realistic two- and three-nucleon interactions and realistic nuclear charge-changing weak currents. The currents include one- and two-body terms induced by $\pi$- and $\rho$-meson exchange, and $N$-to-$\Delta$ excitation, and are constrained to reproduce the empirical value of the Gamow-Teller matrix element in tritium. We investigate the sensitivity of theoretical predictions to current parametrizations of the nucleon axial and induced pseudoscalar form factors as well as to two-body contributions in the weak currents. The large uncertainties in the measured values obtained from bubble-chamber experiments (carried out over 50 years ago) prevent us from drawing any definite conclusions.Comment: 6 pages, 1 figur

Botulinum toxin therapy: functional silencing of salivary disorders.

Botulinum toxin (BTX) is a neurotoxic protein produced by Clostridium botulinum, an anaerobic bacterium. BTX therapy is a safe and effective treatment when used for functional silencing of the salivary glands in disorders such as sialoceles and salivary fistulas that may have a post-traumatic or post-operative origin. BTX injections can be considered in sialoceles and salivary fistulas after the failure of or together with conservative treatments (e.g. antibiotics, pressure dressings, or serial aspirations). BTX treatment has a promising role in chronic sialadenitis. BTX therapy is highly successful in the treatment of gustatory sweating (Frey\u2019s syndrome), and could be considered the gold standard treatment for this neurological disorder

Uncertainty Quantification-Enabled Inversion of Nuclear Euclidean Responses

Nuclear quantum many-body methods rely on integral transform techniques to infer properties of electroweak response functions from ground-state expectation values. Retrieving the energy dependence of these responses is highly non-trivial, especially for quantum Monte Carlo methods, as it requires inverting the Laplace transform -- a notoriously ill-posed problem. In this work, we propose an artificial neural network architecture suitable for accurate response function reconstruction with precise estimation of the uncertainty of the inversion. We demonstrate the capabilities of this new architecture benchmarking it against Maximum Entropy and previously developed neural network methods designed for a similar task, paying particular attention to its robustness against increasing noise in the input Euclidean responses.Comment: 11 pages, 7 figure