On the efficiency of parameter space exploration: A scotogenic case study

Abstract

A common problem in beyond Standard Model phenomenology is the exploration of a multi-dimensional parameter space in view of a large number of constraints. We study and compare two methods applicable to this challenge, namely a Markov Chain Monte Carlo scan (MCMC) and a Deep Neural Network (DNN). We illustrate both methods via their application to different scotogenic frameworks, allowing to extend the Standard Model to include viable dark matter candidates while generating neutrino mass terms at the one-loop level. Our studies allow us to compare the two employed methods, both at the level of phenomenology and at the level of computing effort. We find that, while phenomenologically speaking both methods deliver compatible conclusions, the obtained datasets feature differences at the detail level in the distributions of observables, e.g. the dark matter mass.