In a wafer-modified Bousso-Polchinski model, 99.95% of the 532 million Calabi-Yau fourfold configurations in the Schöller-Skarke database allow vacuum energy spacings of 10^{-120} or smaller, with membrane nucleation transitions dominated by giant flux leaps under thin-wall approximations.
Machine Learning in the String Landscape
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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 $\frac43 \times 2.96 \times 10^{755}$ 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.
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Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.
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Small Vacuum Energy and Tunneling in a Modified Bousso-Polchinski Model
In a wafer-modified Bousso-Polchinski model, 99.95% of the 532 million Calabi-Yau fourfold configurations in the Schöller-Skarke database allow vacuum energy spacings of 10^{-120} or smaller, with membrane nucleation transitions dominated by giant flux leaps under thin-wall approximations.
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Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning
Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.