Deep learning in the heterotic orbifold landscape

DOI10.1016/j.nuclphysb.2019.01.013zbMath1409.81099arXiv1811.05993OpenAlexW2901947642WikidataQ128456916 ScholiaQ128456916MaRDI QIDQ1731668

Erik Parr, Patrick K. S. Vaudrevange, Andreas Mütter

Publication date: 13 March 2019

Published in: Nuclear Physics. B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.05993

Mathematics Subject Classification ID

Unified quantum theories (81V22) String and superstring theories; other extended objects (e.g., branes) in quantum field theory (81T30) Neural nets and related approaches to inference from stochastic processes (62M45) General theory for finite permutation groups (20B05) Topology and geometry of orbifolds (57R18)

Related Items (13)

Data science applications to string theory ⋮ Black holes and the loss landscape in machine learning ⋮ Topological data analysis for the string landscape ⋮ 134 billion intersecting brane models ⋮ Contrast data mining for the MSSM from strings ⋮ Towards the “Shape” of Cosmological Observables and the String Theory Landscape with Topological Data Analysis ⋮ Getting CICY high ⋮ Searching the landscape of flux vacua with genetic algorithms ⋮ Jumping spectra and vanishing couplings in heterotic line bundle standard models ⋮ Algorithmically solving the tadpole problem ⋮ Branes with brains: exploring string vacua with deep reinforcement learning ⋮ Deep learning and k-means clustering in heterotic string vacua with line bundles ⋮ Accessibility measure for eternal inflation: dynamical criticality and higgs metastability

Uses Software

• Orbifolder
• TensorFlow

Cites Work

• Unnamed Item
• A heterotic standard model
• Supersymmetric standard model spectra from RCFT orientifolds
• Dual models of gauge unification in various dimensions
• Reducing the number of candidates to standard model in the \(Z_3\) orbifold
• Machine learning in the string landscape
• Machine learning line bundle cohomologies of hypersurfaces in toric varieties
• Heterotic line bundle standard models
• A mini-landscape of exact MSSM spectra in heterotic orbifolds
• Three-family particle physics models from global F-theory compactifications
• Reducing the Dimensionality of Data with Neural Networks
• Geography of fields in extra dimensions: String theory lessons for particle physics

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