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

TL;DR

This paper compares Markov Chain Monte Carlo and Deep Neural Network methods for exploring multi-dimensional parameter spaces in beyond Standard Model physics, focusing on scotogenic models that include dark matter candidates and neutrino masses.

Contribution

It provides a comparative analysis of MCMC and DNN approaches in the context of scotogenic models, highlighting their efficiency and differences in detailed observable distributions.

Findings

Both methods yield compatible phenomenological conclusions.

Datasets differ in the detailed distributions of observables.

DNNs can be an efficient alternative to MCMC in this context.

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.