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Machine learning in the string landscape. (English) Zbl 1382.81155
Summary: 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.

MSC:
81T30 String and superstring theories; other extended objects (e.g., branes) in quantum field theory
83E30 String and superstring theories in gravitational theory
68T05 Learning and adaptive systems in artificial intelligence
62P35 Applications of statistics to physics
62J12 Generalized linear models (logistic models)
Software:
py-junctions
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