# Learning non-Higgsable gauge groups in 4D F-theory

Wang, Y
Zhang, Z

3 August 2018

## Journal:

Journal of High Energy Physics

## Last Updated:

2020-05-23T00:38:23.32+01:00

## DOI:

10.1007/JHEP08(2018)009

## abstract:

We apply machine learning techniques to solve a specific classification
problem in 4D F-theory. For a divisor $D$ on a given complex threefold base, we
want to read out the non-Higgsable gauge group on it using local geometric
information near $D$. The input features are the triple intersection numbers
among divisors near $D$ and the output label is the non-Higgsable gauge group.
We use decision tree to solve this problem and achieved 85%-98% out-of-sample
accuracies for different classes of divisors, where the data sets are generated
from toric threefold bases without (4,6) curves. We have explicitly generated a
large number of analytic rules directly from the decision tree and proved a
small number of them. As a crosscheck, we applied these decision trees on bases
with (4,6) curves as well and achieved high accuracies. Additionally, we have
trained a decision tree to distinguish toric (4,6) curves as well. Finally, we
present an application of these analytic rules to construct local base
configurations with interesting gauge groups such as SU(3).

927659