Learning nonHiggsable gauge groups in 4D Ftheory
Abstract
We apply machine learning techniques to solve a specific classification problem in 4D Ftheory. For a divisor D on a given complex threefold base, we want to read out the nonHiggsable 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 nonHiggsable gauge group. We use decision tree to solve this problem and achieved 85%98% outofsample 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).
 Authors:

 Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Center for Theoretical Physics, Dept. of Physics
 New York Univ. (NYU), NY (United States). Tandon School of Engineering, Dept. of Finance and Risk Engineering
 Publication Date:
 Research Org.:
 Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC)
 OSTI Identifier:
 1505580
 Grant/Contract Number:
 SC0012567
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of High Energy Physics (Online)
 Additional Journal Information:
 Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2018; Journal Issue: 8; Journal ID: ISSN 10298479
 Publisher:
 Springer Berlin
 Country of Publication:
 United States
 Language:
 English
 Subject:
 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 97 MATHEMATICS AND COMPUTING; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Differential and Algebraic Geometry; FTheory
Citation Formats
Wang, YiNan, and Zhang, Zhibai. Learning nonHiggsable gauge groups in 4D Ftheory. United States: N. p., 2018.
Web. doi:10.1007/jhep08(2018)009.
Wang, YiNan, & Zhang, Zhibai. Learning nonHiggsable gauge groups in 4D Ftheory. United States. doi:https://doi.org/10.1007/jhep08(2018)009
Wang, YiNan, and Zhang, Zhibai. Fri .
"Learning nonHiggsable gauge groups in 4D Ftheory". United States. doi:https://doi.org/10.1007/jhep08(2018)009. https://www.osti.gov/servlets/purl/1505580.
@article{osti_1505580,
title = {Learning nonHiggsable gauge groups in 4D Ftheory},
author = {Wang, YiNan and Zhang, Zhibai},
abstractNote = {We apply machine learning techniques to solve a specific classification problem in 4D Ftheory. For a divisor D on a given complex threefold base, we want to read out the nonHiggsable 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 nonHiggsable gauge group. We use decision tree to solve this problem and achieved 85%98% outofsample 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).},
doi = {10.1007/jhep08(2018)009},
journal = {Journal of High Energy Physics (Online)},
number = 8,
volume = 2018,
place = {United States},
year = {2018},
month = {8}
}
Web of Science
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