论文标题
宇宙通胀和遗传算法
Cosmic Inflation and Genetic Algorithms
论文作者
论文摘要
大量的标准单场慢速通货膨胀模型与所需的E折数量，标量扰动的光谱指数上的电流边界，张量比比率以及通货膨胀量表可以使用遗传算法有效地构建。该设置是模块化的，可以轻松适应以包括进一步的现象学约束。半整合搜索六种多项式电位会导致大约O（300,000）可行的通货膨胀模型。该数据集的分析揭示了对张量与尺度比率为0.0001 <r <0.0004的模型的偏好。我们还考虑涉及余弦和指数术语的潜力。在最后一部分中，我们探索了依靠强化学习和遗传编程的更复杂的搜索方法。尽管在这种情况下，强化学习证明更难使用，但遗传编程方法有可能发现具有新功能形式的众多可行的通货膨胀模型。
Large classes of standard single-field slow-roll inflationary models consistent with the required number of e-folds, the current bounds on the spectral index of scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation can be efficiently constructed using genetic algorithms. The setup is modular and can be easily adapted to include further phenomenological constraints. A semi-comprehensive search for sextic polynomial potentials results in roughly O(300,000) viable models for inflation. The analysis of this dataset reveals a preference for models with a tensor-to-scalar ratio in the range 0.0001 < r < 0.0004. We also consider potentials that involve cosine and exponential terms. In the last part we explore more complex methods of search relying on reinforcement learning and genetic programming. While reinforcement learning proves more difficult to use in this context, the genetic programming approach has the potential to uncover a multitude of viable inflationary models with new functional forms.
