Supervised machine learning for analysing spectra of exoplanetary atmospheres
Supervised machine learning for analysing spectra of exoplanetary atmospheres
复制标题
用于分析系外行星大气光谱的监督机器学习
作者：
Pablo Márquez;C. Fisher;R. Sznitman;K. Heng
机器学习的使用在天文学中越来越普遍，但在系外行星大气层的研究中仍然很少见。给定系外行星大气的光谱，在真实的时间内扫描多参数空间以找到最佳拟合模型4 -6。这种技术被称为大气恢复，起源于地球和行星科学7。这种方法非常耗时，并且必然在物理和化学现实主义与计算可行性之间存在折衷。机器学习以前曾用于确定模型中包含哪些分子，但检索本身仍然使用标准方法进行。在这里，我们报告了一个适应的“随机森林”方法的监督机器学习9，10，训练的预计算网格的大气模型，检索完整的后验分布的丰度的分子和云的不透明度。使用预先计算的网格允许将大部分计算负担转移到离线。我们使用五参数模型（温度，恒定的云不透明度和水，氨和氰化氢分子的体积混合比或相对丰度）11在热气体巨型系外行星WASP-12 b的透射光谱上展示了我们的技术。我们得到的结果与标准的嵌套抽样检索方法一致。我们还估计了测量光谱对模型参数的敏感性，并且我们能够量化光谱的信息含量。我们的方法可以直接使用更复杂的大气模型来解释光谱的集合，而无需重新训练随机森林。提出了一种使用监督“随机森林”机器学习的系外行星大气检索方法，该方法比标准技术耗时更少。对WASP-12 b的哈勃光谱进行了测试，得到了与标准大气反演一致的结果。
The use of machine learning is becoming ubiquitous in astronomy1–3, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find the best-fit model4–6. Known as atmospheric retrieval, this technique originates in the Earth and planetary sciences7. Such methods are very time-consuming, and by necessity there is a compromise between physical and chemical realism and computational feasibility. Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods8. Here, we report an adaptation of the ‘random forest’ method of supervised machine learning9,10, trained on a precomputed grid of atmospheric models, which retrieves full posterior distributions of the abundances of molecules and the cloud opacity. The use of a precomputed grid allows a large part of the computational burden to be shifted offline. We demonstrate our technique on a transmission spectrum of the hot gas-giant exoplanet WASP-12b using a five-parameter model (temperature, a constant cloud opacity and the volume mixing ratios or relative abundances of molecules of water, ammonia and hydrogen cyanide)11. We obtain results consistent with the standard nested-sampling retrieval method. We also estimate the sensitivity of the measured spectrum to the model parameters, and we are able to quantify the information content of the spectrum. Our method can be straightforwardly applied using more sophisticated atmospheric models to interpret an ensemble of spectra without having to retrain the random forest. A method of atmospheric retrieval for exoplanets that uses supervised ‘random forest’ machine learning, less time-consuming than standard techniques, is presented. Tests on Hubble spectra of WASP-12b give results consistent with standard atmospheric retrievals.
影响因子：
4.8
作者：
Barber, R. J.;Strange, J. K.;Tennyson, Jonathan
通讯作者：
Tennyson, Jonathan