Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
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DOI：
10.1002/prop.202200034
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
S. Abel;A. Constantin;T. R. Harvey;A. Lukas
中科院分区：
文献类型：
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作者：
S. Abel;A. Constantin;T. R. Harvey;A. Lukas
弦图景的广泛性，以及寻找与粒子物理学观察到的特征相匹配的解决方案的难度，引发了人们对弦理论预测能力的严重质疑。然而，现代的优化和搜索方法可以显著改善构建弦理论标准模型的前景。在这篇文章中，我们仔细研究了由Calabi-Yau三折叠和单元丛的紧致组成的杂化弦景观的一角，并证明了遗传算法可以成功地用于产生无反常的超对称SO(10)$SO(10)$GUT，其中有三个费米子家族具有合适的成分来适应标准模型。将该方法与强化学习方法进行了比较，发现两种方法具有相似的效果，但具有一定的互补性。
The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and search can, however, significantly improve the prospects of constructing the standard model in string theory. In this paper we scrutinise a corner of the heterotic string landscape consisting of compactifications on Calabi‐Yau three‐folds with monad bundles and show that genetic algorithms can be successfully used to generate anomaly‐free supersymmetric SO(10)$SO(10)$ GUTs with three families of fermions that have the right ingredients to accommodate the standard model. We compare this method with reinforcement learning and find that the two methods have similar efficacy but somewhat complementary characteristics.