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arXiv 2203.04201astro-ph.EPastro-ph.IMcs.LG
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跟着水走:用Photometry和机器学习在类地系外行星上寻找水、雪和云

Follow the Water: Finding Water, Snow and Clouds on Terrestrial Exoplanets with Photometry and Machine Learning

University of Toronto(多伦多大学);Cornell University(康奈尔大学)

Dang Pham, Lisa Kaltenegger

摘要:

地球上的所有生命都需要水。美国宇航局对水的探索将水与寻找宇宙中的生命联系起来。像JWST这样的望远镜和像HabEx,LUVOIR和Origins这样的任务概念旨在通过光谱来探测岩石系外行星。然而,光谱学仍然是时间密集型的,因此,初始表征对于目标的优先级是至关重要的。在这里,我们研究机器学习作为一种工具,通过三种形式的类地系外行星上的宽带滤波器反射光度通量来评估水的存在:海水,水云和雪;基于具有6个主要表面的寒冷类地行星的53,130个光谱。XGBoost是一种著名的机器学习算法,在S/N $\gtrsim 20$中检测雪或云的存在时达到90%以上的平衡准确度,在S/N $\gtrsim 30$中检测液态海水时达到70%以上的平衡准确度。最后,我们进行模拟贝叶斯分析与马尔可夫链蒙特卡罗与五个过滤器确定,以获得准确的表面成分,以测试检索的可行性。结果表明,使用机器学习从宽带滤波光度法中识别系外行星表面的水,为不同形式的水提供了一个有前途的初始表征工具。计划中的小型和大型望远镜任务可以利用这一点来帮助他们优先考虑时间密集的后续观测目标。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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