A Transformer RL agent is trained to generate valid heterotic line bundle sums on CICYs that satisfy gauge embedding, anomaly cancellation, poly-stability, chirality, and no-exotics constraints.
Machine Learning Line Bundle Cohomologies of Hypersurfaces in Toric Varieties
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Different techniques from machine learning are applied to the problem of computing line bundle cohomologies of (hypersurfaces in) toric varieties. While a naive approach of training a neural network to reproduce the cohomologies fails in the general case, by inspecting the underlying functional form of the data we propose a second approach. The cohomologies depend in a piecewise polynomial way on the line bundle charges. We use unsupervised learning to separate the different polynomial phases. The result is an analytic formula for the cohomologies. This can be turned into an algorithm for computing analytic expressions for arbitrary (hypersurfaces in) toric varieties.
fields
hep-th 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hilbert functions of Koszul maps turn empirical chamber-wise polynomial formulae for line bundle cohomology on CICY threefolds into explicit analytic or finite-box certified statements.
citing papers explorer
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Exploring Line Bundle Standard Models with Transformers
A Transformer RL agent is trained to generate valid heterotic line bundle sums on CICYs that satisfy gauge embedding, anomaly cancellation, poly-stability, chirality, and no-exotics constraints.
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Hilbert Functions and Line Bundle Cohomology on CICY Threefolds
Hilbert functions of Koszul maps turn empirical chamber-wise polynomial formulae for line bundle cohomology on CICY threefolds into explicit analytic or finite-box certified statements.