Machine learning for complete intersection Calabi-Yau manifolds: a methodological study

TL;DR

This paper applies machine learning techniques, including neural networks and linear regression, to predict Hodge numbers of complete intersection Calabi-Yau threefolds, improving accuracy over previous methods and demonstrating ML's potential in string theory geometry analysis.

Contribution

It introduces a comprehensive ML workflow for predicting Calabi-Yau Hodge numbers, achieving higher accuracy and showcasing neural networks' usefulness in string theory geometry.

Findings

Achieved 97-99% accuracy for h^{1,1} predictions.

Reached nearly 100% accuracy for new dataset with linear regression.

Improved prediction accuracy for h^{2,1} over previous neural network approaches.

Abstract

We revisit the question of predicting both Hodge numbers $h^{1,1}$ and $h^{2,1}$ of complete intersection Calabi-Yau (CICY) 3-folds using machine learning (ML), considering both the old and new datasets built respectively by Candelas-Dale-Lutken-Schimmrigk / Green-H\"ubsch-Lutken and by Anderson-Gao-Gray-Lee. In real world applications, implementing a ML system rarely reduces to feed the brute data to the algorithm. Instead, the typical workflow starts with an exploratory data analysis (EDA) which aims at understanding better the input data and finding an optimal representation. It is followed by the design of a validation procedure and a baseline model. Finally, several ML models are compared and combined, often involving neural networks with a topology more complicated than the sequential models typically used in physics. By following this procedure, we improve the accuracy of ML computations for Hodge numbers with respect to the existing literature. First, we obtain 97% (resp. 99%) accuracy for $h^{1,1}$ using a neural network inspired by the Inception model for the old dataset, using only 30% (resp. 70%) of the data for training. For the new one, a simple linear regression leads to almost 100% accuracy with 30% of the data for training. The computation of $h^{2,1}$ is less successful as we manage to reach only 50% accuracy for both datasets, but this is still better than the 16% obtained with a simple neural network (SVM with Gaussian kernel and feature engineering and sequential convolutional network reach at best 36%). This serves as a proof of concept that neural networks can be valuable to study the properties of geometries appearing in string theory.