Collaborative Research: Catch the waves - a machine learning approach to map brown dwarf and imaged exoplanet atmospheres in 3D

合作研究：捕捉波浪 - 一种机器学习方法，以 3D 方式绘制褐矮星和成像系外行星大气层

• 批准号: 2206317
• 负责人: Jonathan Fortney
• 金额: $ 10.37万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206317&HistoricalAwards=false
• 关键词: Collaborative; Research; Catch; waves; machine

Atmospheres and their clouds are crucial for how exoplanet and brown dwarfs cool down over time. Atmospheres and clouds are 3D. The typical techniques used to model them though, require a lot of computer time. To understand their properties they generally need to be simplified to 1D or 2D structures. However, knowing the 3D structure of clouds is important to understand how they affect the atmosphere. A team led by the University of Central Florida and the University of California-Santa Cruz will develop a new method that reduces the computer time needed to model clouds and atmospheres. This technique will allow a characterization of brown dwarf and imaged exoplanet atmospheres in 3D. This technique will help the study of how clouds change with important atmospheric properties (age, gravity, temperature and metallicity). It will also help test techniques that predict the weather in these atmospheres. This work will thus improve understanding of atmospheres. This work will support a graduate and an undergraduate student, and will form the basis for their thesis. Hands-on activities and videos will be created to familiarize K-12 students with atmospheres and programming.The goal of this proposal is to enable the 3D characterization of brown dwarf and imaged exoplanet atmospheres. In order to do this a surrogate radiative transfer code will be created that will use the power of neural networks to model spectra of atmospheres in a fraction of the time current codes need. The surrogate radiative transfer code will be used to create a 3D mapping code that fits time resolved observations in a Bayesian framework. The code will be cross-validated on output from an independent, state-of-the-art General Circulation Model. Ground-based telescopes already give excellent data that enable the 3D mapping of these atmospheres and in the next decades the number of appropriate data will increase considerably. The code will be applied on existing observations to create the first 3D maps of brown dwarf atmospheres. The proposed work will create tools that will uniquely enable comparative climatology in the next decades. The surrogate radiative transfer code and the 3D mapping code will be provided open source to the community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

气氛及其云对系外星球和棕色矮小的矮小至关重要。气氛和云为3D。但是，用于对它们进行建模的典型技术需要大量的计算机时间。要了解它们的属性，通常需要将它们简化为1D或2D结构。但是，了解云的3D结构对于了解它们如何影响大气很重要。 由中部佛罗里达大学和加利福尼亚大学圣克鲁斯大学领导的团队将开发一种新方法，以减少建模云和气氛所需的计算机时间。该技术将允许在3D中表征棕色矮人和成像系外行星的大气。该技术将有助于研究云如何随重要的大气特性（年龄，重力，温度和金属性）而变化。它还将有助于预测这些气氛中天气的测试技术。因此，这项工作将改善对气氛的理解。这项工作将支持研究生和本科生，并将构成其论文的基础。将创建动手活动和视频，以使K-12学生熟悉氛围和编程。该提案的目的是使Brown Dwarf的3D表征和成像外部活动的氛围。为了做到这一点，将创建一个替代辐射转移代码，该代码将利用神经网络的力量来建模当前代码所需时间的一小部分大气光谱。替代辐射传输代码将用于创建一个适合贝叶斯框架中时间解决的观测值的3D映射代码。该代码将对独立的，最先进的一般循环模型的输出进行交叉验证。地面望远镜已经提供了出色的数据，可实现这些大气的3D映射，在接下来的几十年中，适当数据的数量将大大增加。该代码将应用于现有观测值，以创建棕色矮人气氛的前3D地图。拟议的工作将创建工具，这些工具将在未来几十年中独特地实现比较气候。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，该奖项反映了NSF的法定任务，该奖项反映了NSF的法定任务。