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

Journal Article · 03 August 2018 · Journal of High Energy Physics (Online)

DOI: https://doi.org/10.1007/jhep08(2018)009 · OSTI ID:1505580

Wang, Yi-Nan ^[1]; Zhang, Zhibai ^[2]

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1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics, Dept. of Physics
2. New York Univ. (NYU), NY (United States). Tandon School of Engineering, Dept. of Finance and Risk Engineering

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).

• Research Organization: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0012567
• OSTI ID: 1505580
• Journal Information: Journal of High Energy Physics (Online), Vol. 2018, Issue 8; ISSN 1029-8479
• Publisher: Springer BerlinCopyright Statement
• Country of Publication: United States
• Language: English