Machine Learning in the String Landscape

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.

35 pages, 4 figures

References in corpus (25)

• Evidence for F-Theory
• Quantization of Four-form Fluxes and Dynamical Neutralization of the Cosmological Constant
• Compactifications of F-Theory on Calabi--Yau Threefolds -- II
• Distributions of flux vacua
• Counting Flux Vacua
• Yukawas, G-flux, and Spectral Covers from Resolved Calabi-Yau's
• Computational complexity of the landscape I
• Toric bases for 6D F-theory models
• Evolving neural networks with genetic algorithms to study the String Landscape
• Box Graphs and Singular Fibers
• The Tate Form on Steroids: Resolution and Higher Codimension Fibers
• On the Hodge structure of elliptically fibered Calabi-Yau threefolds
• Matter From Geometry Without Resolution
• Non-Higgsable clusters for 4D F-theory models
• Calabi-Yau threefolds with large h^{2, 1}
• ${\mathbb{P}}^1$-bundle bases and the prevalence of non-Higgsable structure in 4D F-theory models
• A Monte Carlo exploration of threefold base geometries for 4d F-theory vacua
• Geometric constraints in dual F-theory and heterotic string compactifications
• The Vertical, the Horizontal and the Rest: anatomy of the middle cohomology of Calabi-Yau fourfolds and F-theory applications
• Non-Higgsable QCD and the Standard Model Spectrum in F-theory
• On the computation of non-perturbative effective potentials in the string theory landscape -- IIB/F-theory perspective
• Strong Coupling in F-theory and Geometrically Non-Higgsable Seven-branes
• Box Graphs and Resolutions II: From Coulomb Phases to Fiber Faces
• Non-Abelian Gauge Symmetry and the Higgs Mechanism in F-theory
• Statistics of Flux Vacua for Particle Physics

Cited by in corpus (67)

• Machine learning and the physical sciences
• Deep Learning and AdS/CFT
• Machine Learning CICY Threefolds
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• AdS/CFT as a deep Boltzmann machine
• Machine Learning meets Quantum Foundations: A Brief Survey
• Machine learning Calabi-Yau metrics
• Scanning the skeleton of the 4D F-theory landscape
• Searching the Landscape of Flux Vacua with Genetic Algorithms
• Deep Learning and AdS/QCD
• Machine Learning Line Bundle Cohomology
• A Quadrillion Standard Models from F-theory
• Computational Complexity of Vacua and Near-Vacua in Field and String Theory
• Getting CICY High
• Vacuum Selection from Cosmology on Networks of String Geometries
• Quiver Mutations, Seiberg Duality and Machine Learning
• Machine Learning Calabi-Yau Hypersurfaces
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• Machine Learning $\mathcal{N}=8,D=5$ Gauged Supergravity
• Estimating Calabi-Yau Hypersurface and Triangulation Counts with Equation Learners
• Explore and Exploit with Heterotic Line Bundle Models
• Machine-Learning Dessins d'Enfants: Explorations via Modular and Seiberg-Witten Curves
• Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
• Formulae for Line Bundle Cohomology on Calabi-Yau Threefolds
• Deep learning and k-means clustering in heterotic string vacua with line bundles
• On Axion Reheating in the String Landscape
• Emergent Quantum Mechanics in an Introspective Machine Learning Architecture
• Statistical Predictions in String Theory and Deep Generative Models
• Accessibility Measure for Eternal Inflation: Dynamical Criticality and Higgs Metastability
• Moduli-dependent KK towers and the swampland distance conjecture on the quintic Calabi-Yau manifold
• Machine Learning and Algebraic Approaches towards Complete Matter Spectra in 4d F-theory
• Learning non-Higgsable gauge groups in 4D F-theory
• Artificial Neural Network in Cosmic Landscape
• Machine-Learning the Sato--Tate Conjecture
• Applying machine learning to the Calabi-Yau orientifolds with string vacua
• Calabi-Yau Metrics, Energy Functionals and Machine-Learning
• The landscape of promising non-supersymmetric string models
• Breeding realistic D-brane models
• Active learning BSM parameter spaces
• Disentangling a Deep Learned Volume Formula
• Kahler Moduli Stabilization and the Propagation of Decidability
• A Triumvirate of AI Driven Theoretical Discovery
• On the Scarcity of Weak Coupling in the String Landscape
• 134 Billion Intersecting Brane Models
• Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality
• Predicting the orbifold origin of the MSSM
• Ising Machines for Diophantine Problems in Physics
• Machine learning Sasakian and $G_2$ topology on contact Calabi-Yau $7$-manifolds
• Learning to Inflate
• Polytopes and Machine Learning
• Machine Learning Regularization for the Minimum Volume Formula of Toric Calabi-Yau 3-folds
• Brill-Noether-general Limit Root Bundles: Absence of vector-like Exotics in F-theory Standard Models
• Level Crossings, Attractor Points and Complex Multiplication
• Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning
• Universes as Big Data
• Learning BPS Spectra and the Gap Conjecture
• Constructing and Machine Learning Calabi-Yau Five-folds
• Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
• Generative AI for Brane Configurations and Coamoeba
• Hitchhiker's Guide to the Swampland: The Cosmologist's Handbook to the string-theoretical Swampland Programme
• Black holes and the loss landscape in machine learning
• Joint Statistics of Cosmological Constant and SUSY Breaking in Flux Vacua with Nilpotent Goldstino
• Improved statistics for F-theory standard models
• Calabi-Yau Four/Five/Six-folds as ${\mathbb{P}}_{\text{w}}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation
• Beyond holography: the entropic quantum gravity foundations of image processing
• Row and column detection complexities of character tables
• Learning 3-Manifold Triangulations