An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
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DOI：
10.3847/1538-3881/ab2390
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发表时间：
2019-07-01
影响因子：
5.3
通讯作者：
Angerhausen, Daniel
中科院分区：
文献类型：
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作者：
Cobb, Adam D.;Himes, Michael D.;Angerhausen, Daniel
机器学习（ML）现在被用于天体物理学的许多领域，从探测开普勒凌日信号中的系外行星到移除望远镜系统。最近的工作证明了使用ML算法进行大气检索的潜力，通过实施随机森林（RF）在几秒钟内执行检索，与传统的计算成本高昂的嵌套采样检索方法一致。我们扩展了他们的方法，提出了一个新的ML模型plan-net，该模型基于贝叶斯神经网络（bnn）的集合，对相同的合成透射光谱数据集产生比RF更准确的推断。我们证明了集成比单一模型提供更高的精度和更稳健的不确定性。除了首次使用bnn进行大气检索外，我们还为bnn引入了一个新的损失函数，用于学习模型输出之间的相关性。重要的是，我们表明，设计ML模型来明确地结合特定领域的知识，既提高了性能，又通过推断检索到的大气参数的协方差提供了额外的见解。我们将plan-net应用于哈勃太空望远镜宽视场相机3的WASP-12b透射光谱，获得了与文献一致的等温温度和水丰度。我们强调，我们的方法是灵活的，可以扩展到更高分辨率的光谱和更多的大气参数。
Machine learning (ML) is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using ML algorithms for atmospheric retrieval by implementing a random forest (RF) to perform retrievals in seconds that are consistent with the traditional, computationally expensive nested-sampling retrieval method. We expand upon their approach by presenting a new ML model, plan-net, based on an ensemble of Bayesian neural networks (BNNs) that yields more accurate inferences than the RF for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use BNNs for atmospheric retrieval, we also introduce a new loss function for BNNs that learns correlations between the model outputs. Importantly, we show that designing ML models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply plan-net to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters.