Revealing systematics in phenomenologically viable flux vacua with reinforcement learning

Abstract

The organising principles underlying the structure of phenomenologically viable string vacua can be accessed by sampling such vacua. In many cases this is prohibited by the computational cost of standard sampling methods in the high dimensional model space.Here we show how this problem can be alleviated using reinforcement learning techniques to explore string flux vacua. We demonstrate in the case of the type IIB flux landscape that vacua with requirements on the expectation value of the superpotential and the string coupling can be sampled significantly faster by using reinforcement learning than by using metropolis or random sampling. Our analysis is on conifold and symmetric torus background geometries. We show that reinforcement learning is able to exploit successful strategies for identifying such phenomenologically interesting vacua. The strategies are interpretable and reveal previously unknown correlations in the flux landscape.

Applying machine learning to the Calabi-Yau orientifolds with string vacua

“…This example defines an MPCP desingularized ambient toric variety with weight matrix W given by x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 x 10 x 11 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 (10) There are 12 different MPCP triangulations and we check the first one with Stanley-Reisner (SR) ideal as:…”

“…Machine learning has been a good implement in theoretical physics research and leads to fruitful results during the last couple of years. With the help of machine learning people are able to deal with problems with more computational efficiency, especially the problems involving big data, for example, study the landscape of string flux vacua [8][9][10][11][12][13][14][15][16][17] as well as F-theory compactifications [18][19][20]. This technique allows people to learn lots of quantities of Calabi-Yau manifolds, from its toric building blocks like the polytope structure [21,22] and triangulations [23,24], to the calculation of Hodge numbers [25][26][27][28], numerical metrics [29][30][31][32] and line bundle cohomologies [33,34].…”

On the Search for Low W0

“…• How does the subclass fit within the larger ensemble of the full set of vacua? More specifically, what can we say about the set from the point of view of the statistical approach to string phenomenology [30][31][32][33][34] (see [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] for studies in various settings in this context)? • How can one carry out exhaustive searches which will allow us to have a complete understanding of the vacua in this set (and their physics)?…”

On the Search for Low $W_0$

“…• How does the subclass fit within the larger ensemble of the full set of vacua? More specifically, what can we say about the set from the point of view of the statistical approach to string phenomenology [30][31][32][33][34] (see [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] for studies in various settings in this context)?…”