Abstract: The framed Standard Model (FSM) predicts a 0(+) boson with mass around 20 MeV in the “hidden sector,” which mixes at tree level with the standard Higgs hW and hence acquires small couplings to quarks and leptons which can be calculated in the FSM apart from the mixing parameter rho Uh. The exchange of this mixed state U will contribute to g – 2 and to the Lamb shift. By adjusting rho Uh alone, it is found that the FSM can satisfy all present experimental bounds on the g – 2 and Lamb shift anomalies for μand e, and for the latter for both hydrogen and deuterium. The FSM predicts also a 1(-) boson in the “hidden sector” with a mass of 17 MeV, that is, right on top of the Atomki anomaly X. This mixes with the photon at 1-loop level and couples thereby like a dark photon to quarks and leptons. It is however a compound state and is thought likely to possess additional compound couplings to hadrons. By adjusting the mixing parameter and the X's compound coupling to nucleons, the FSM can reproduce the production rate of the X in beryllium decay as well as satisfy all the bounds on X listed so far in the literature. The above two results are consistent in that the U, being 0(+), does not contribute to the Atomki anomaly if parity and angular momentum are conserved, while X, though contributing to g – 2 and Lamb shift, has smaller couplings than U and can, at first instance, be neglected there. Thus, despite the tentative nature of the three anomalies in experiment on the one hand and of the FSM as theory on the other, the accommodation of the former in the latter has strengthened the credibility of both. Indeed, if this FSM interpretation were correct, it would change the whole aspect of the anomalies from just curiosities to windows into a vast hitherto hidden sector comprising at least in part the dark matter which makes up the bulk of our universe.

Abstract: Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MULTINEST, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration previously used in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MULTINEST configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MULTINEST is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded.