The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parametrize the most general family of degeneracies among the input atmospheric parameters which affect the characterization of an exoplanet atmosphere through transit spectroscopy.

翻译：新发现的外行星的物理特性和大气化学成分往往从其从辐射转移的复杂数字模型中获得的中转光谱中推断出来。或者,简单的分析表达方式为相关的大气过程提供了有洞察力的物理直觉。深层次的学习革命打开了直接得出这种分析结果的大门,并采用了与数据相适应的计算机算法。作为概念的证明,我们成功地展示了在通用热木星外行星的中转射线的合成数据中使用象征性回归法来得出相应的分析公式。作为预处理步骤,我们利用量分析来确定相关变量的无维的组合,并减少独立投入的数量,从而改进了象征性回归的性能。量分析还使我们得以数学地得出并适当匹配了输入大气参数中影响通过中转光谱分析外行星大气层特征的最一般的变异性组。