.png){kind=icon}
返回
Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification
.png){kind=icon}
OA
AI
DOI：10.1093/mnras/staa978.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
One of the principal bottlenecks to atmosphere characterization in the era of all-sky surveys is the availability of fast, autonomous, and robust atmospheric retrieval methods. We present a new approach using unsupervised machine learning to generate informed priors for retrieval of exoplanetary atmosphere parameters from transmission spectra. We use principal component analysis (PCA) to efficiently compress the information content of a library of transmission spectra forward models generated using the PLATON package. We then apply a k-means clustering algorithm in PCA space to segregate the library into discrete classes. We show that our classifier is almost always able to instantaneously place a previously unseen spectrum into the correct class, for low-to-moderate spectral resolutions, R, in the range R = 30-300 and noise levels up to 10 per cent of the peak-to-trough spectrum amplitude. The distribution of physical parameters for all members of the class therefore provides an informed prior for standard retrieval methods such as nested sampling. We benchmark our informed-prior approach against a standard uniform-prior nested sampler, finding that our approach is up to a factor of 2 faster, with negligible reduction in accuracy. We demonstrate the application of this method to existing and near-future observatories, and show that it is suitable for real-world application. Our general approach is not specific to transmission spectroscopy and should be more widely applicable to cases that involve the repetitive fitting of trusted high-dimensional models to large data catalogues, including beyond exoplanetary science.
Keyword:
methods: data analysis
methods: statistical
planets and satellites: atmospheres
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
7.1W
被引数:
25.0W
机构
引用论文
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval.png)
ASTRONOMICAL JOURNALOAAI
ASTRONOMICAL JOURNALIF5.1
OAAI分享收藏Association Between Plasma Trans-Fatty Acid Concentrations and Measures of Glucose Homeostasis and Cardiovascular Risk Factors in Adults in NHANES 1999-2000Angiology
AngiologyIF0
PREAI
分享收藏
查看更多内容