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Journal of High Energy Physics

, 2017:157 | Cite as

Machine learning in the string landscape

  • Jonathan Carifio
  • James Halverson
  • Dmitri Krioukov
  • Brent D. Nelson
Open Access
Regular Article - Theoretical Physics

Abstract

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of \( \frac{4}{3}\times 2.96\times {10}^{755} \) F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

Keywords

D-branes F-Theory Superstring Vacua 

Notes

Open Access

This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Jonathan Carifio
    • 1
  • James Halverson
    • 1
  • Dmitri Krioukov
    • 1
    • 2
    • 3
  • Brent D. Nelson
    • 1
  1. 1.Department of PhysicsNortheastern UniversityBostonU.S.A.
  2. 2.Department of MathematicsNortheastern UniversityBostonU.S.A.
  3. 3.Department of Electrical and Computer EngineeringNortheastern UniversityBostonU.S.A.

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