SciPost Submission Page
On the efficiency of parameter space exploration: A scotogenic case study
by Ugo de Noyers, Mathis Dubau, Björn Herrmann, Olivier Arnaez
Submission summary
| Authors (as registered SciPost users): | Björn Herrmann |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2512.06378v1 (pdf) |
| Date submitted: | Dec. 12, 2025, 6:29 p.m. |
| Submitted by: | Björn Herrmann |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
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.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block