Inflation is a highly favoured theory for the early Universe. It is compatible with current observations of the cosmic microwave background and large scale structure and is a driver in the quest to detect primordial gravitational waves. It is also, given the current quality of the data, highly under-determined with a large number of candidate implementations. We use a new method in symbolic regression to generate all possible simple scalar field potentials for one of two possible basis sets of operators. Treating these as single-field, slow-roll inflationary models we then score them with an information-theoretic metric ("minimum description length") that quantifies their efficiency in compressing the information in the Planck data. We explore two possible priors on the parameter space of potentials, one related to the functions' structural complexity and one that uses a Katz back-off language model to prefer functions that may be theoretically motivated. This enables us to identify the inflaton potentials that optimally balance simplicity with accuracy at explaining the Planck data, which may subsequently find theoretical motivation. Our exploratory study opens the door to extraction of fundamental physics directly from data, and may be augmented with more refined theoretical priors in the quest for a complete understanding of the early Universe.

翻译：暴胀是早期宇宙高度青睐的理论。它与当前对宇宙微波背景和大尺度结构的观测结果兼容，并推动着对原初引力波的探测。同时，鉴于现有数据质量，暴胀模型高度欠定，存在大量候选实现方案。我们采用符号回归新方法，针对两种可能算子基组之一，生成所有可能的简单标量场势。将这些势视为单场慢滚暴胀模型后，我们使用信息论度量（"最小描述长度"）对其评分，该度量量化了模型压缩普朗克数据信息的效率。我们探索了势参数空间的两种可能先验：一种与函数结构复杂度相关，另一种利用Katz回退语言模型优先选择可能具有理论动机的函数。这使我们能够识别出在解释普朗克数据时最优平衡简洁性与准确性的暴胀势，这些势后续可能找到理论动机。本探索性研究为直接从数据中提取基础物理学开启了大门，并可通过引入更精细的理论先验来增强，以最终完整理解早期宇宙。