论文标题
跟随水：在陆地系外行星上找到水，雪和云，并使用光度法和机器学习
Follow the Water: Finding Water, Snow and Clouds on Terrestrial Exoplanets with Photometry and Machine Learning
论文作者
论文摘要
地球上的所有生命都需要水。 NASA追踪水的追求将水与宇宙中的生活联系起来。 JWST和Habex，Luvoir和Origins等任务概念之类的望远镜旨在从光谱上表征岩石外行星。但是，光谱学仍然是时地进行的，因此，初始表征对于目标的优先级至关重要。 在这里，我们将机器学习作为一种工具来评估通过宽带滤波器反映的光度通量在地球样系外行星上以三种形式进行的工具：海水，水云和雪；基于53,130个寒冷，地球状的行星，具有6个主要表面。 Xgboost是一种著名的机器学习算法，在检测S/N $ \ gtrsim 20 $的雪或云方面达到了90 \％的平衡精度，而S/N $ \ gtrsim 30 $的S/N $ \ GTRSIM 20 $和70 \％的液体水平为70 \％。最后，我们使用马尔可夫链蒙特卡洛进行模拟贝叶斯分析，并确定五个过滤器，以得出精确的表面组成，以测试检索可行性。 结果表明，使用机器学习从宽带过滤器光度法中识别外科行星表面上的水提供了一种有希望的不同形式的水的初始特征工具。计划中的小型和大型望远镜任务可以用它来帮助他们优先考虑对时间启动的随访观察的优先级。
All life on Earth needs water. NASA's quest to follow the water links water to the search for life in the cosmos. Telescopes like JWST and mission concepts like HabEx, LUVOIR and Origins are designed to characterise rocky exoplanets spectroscopically. However, spectroscopy remains time-intensive and therefore, initial characterisation is critical to prioritisation of targets. Here, we study machine learning as a tool to assess water's existence through broadband-filter reflected photometric flux on Earth-like exoplanets in three forms: seawater, water-clouds and snow; based on 53,130 spectra of cold, Earth-like planets with 6 major surfaces. XGBoost, a well-known machine learning algorithm, achieves over 90\% balanced accuracy in detecting the existence of snow or clouds for S/N$\gtrsim 20$, and 70\% for liquid seawater for S/N $\gtrsim 30$. Finally, we perform mock Bayesian analysis with Markov-chain Monte Carlo with five filters identified to derive exact surface compositions to test for retrieval feasibility. The results show that the use of machine learning to identify water on the surface of exoplanets from broadband-filter photometry provides a promising initial characterisation tool of water in different forms. Planned small and large telescope missions could use this to aid their prioritisation of targets for time-intense follow-up observations.
