Explore and Exploit with Heterotic Line Bundle Models

Abstract

We use deep reinforcement learning to explore a class of heterotic SU(5) GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds. We perform several experiments where A3C agents are trained to search for such models. These agents significantly outperform random exploration, in the most favourable settings by a factor of 1700 when it comes to finding unique models. Furthermore, we find evidence that the trained agents also outperform random walkers on new manifolds. We conclude that the agents detect hidden structures in the compactification data, which is partly of general nature. The experiments scale well with h (1,1) , and may thus provide the key to model building on CICYs with large h (1,1) . 1 We will refer to such compactifications as 'realistic models' below.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.

Heterotic line bundle models on generalized complete intersection Calabi Yau manifolds

“…Recently, promising results have been obtained from employing techniques from Data Science to solve geometrical problems. Related to heterotic line bundle models, success was reported in finding realistic configurations on CICYs using reinforcement learning [74] and identifying clusters of models using auto-encoders [21,75].…”

Quark Mass Models and Reinforcement Learning

“…[4][5][6][7][8][9][10][11]), where data sets which arise in physics or related areas of mathematics have been used to train neural networks. However, there has also been some interesting work using reinforcement learning (RL), particular in relation to string model building [12,13].…”

Deep learning and k-means clustering in heterotic string vacua with line bundles

“…The deep learning is an attractive method not only for image recognition, but also for applications to string theory as well as particle physics. So far, neural networks have been applied to explore the vacuum structure of string theory [1][2][3][4], for instance, a conjecture for the gauge group rank in F-theory compactifications [5], identification of fertile islands in the toroidal orbifold landscape [6], the prediction of the Hodge numbers of Calabi-Yau (CY) manifolds [7], exploring Type IIA compactifications with intersecting D6-branes [8] and landscape of Type IIB flux vacua [9] and E 8 ×E 8 heterotic line bundle models [10], and finding the numerical CY metric [11]. (For more details, see, ref.…”

“…A decision tree employed in[6] is applicable to our analysis which is valid to approximate the classification of models, but it is beyond our purpose.7 See for the recent discussion of E8 × E8 heterotic line bundle models using deep reinforcement learning, Ref [10]…”