How natural are Froggatt-Nielsen Models?
Abstract
The Froggatt-Nielsen (FN) mechanism aims at a natural explanation of the surprising hierarchical structure of the masses and mixings of Standard Model quarks and leptons. We test this claimed naturalness against various known and some original measures of fine-tuning, including various local measures as well as Bayes fac- tors. The FN models serve as a fruitful laboratory for testing and comparing these measures. We find that the standard Barbieri-Giudice metric is overestimating nat- uralness in many cases, as it does not sufficiently account for the failure of a model to be realized at order-one parameter values, even though the latter are favoured by an appropriate prior distribution. As a side product of our study, we present simple geometric expressions for the posterior distributions of the parameters on the manifold of best-fit values.
Related
BibTeX
@misc{gersdorff2026natural,
title = {{How natural are Froggatt-Nielsen Models?}},
author = {Gero von Gersdorff and Lucas Modesto},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38705},
eprint = {2610.38705},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38705},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - How natural are Froggatt-Nielsen Models?
AU - Gero von Gersdorff
AU - Lucas Modesto
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38705
UR - https://arxiv.org/abs/2610.38705
AB - The Froggatt-Nielsen (FN) mechanism aims at a natural explanation of the surprising hierarchical structure of the masses and mixings of Standard Model quarks and leptons. We test this claimed naturalness against various known and some original measures of fine-tuning, including various local measures as well as Bayes fac- tors. The FN models serve as a fruitful laboratory for testing and comparing these measures. We find that the standard Barbieri-Giudice metric is overestimating nat- uralness in many cases, as it does not sufficiently account for the failure of a model to be realized at order-one parameter values, even though the latter are favoured by an appropriate prior distribution. As a side product of our study, we present simple geometric expressions for the posterior distributions of the parameters on the manifold of best-fit values.
ER -