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: 2205913
• 负责人: Theodora Karalidi
• 金额: $ 41.65万
• 依托单位: The University of Central Florida Board of Trustees
• 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=2205913&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.

大气层及其云对于系外行星和褐矮星如何随着时间的推移冷却至关重要。大气和云是 3D 的。然而，用于建模的典型技术需要大量的计算机时间。为了理解它们的属性，通常需要将它们简化为一维或二维结构。然而，了解云的 3D 结构对于了解它们如何影响大气非常重要。 由中佛罗里达大学和加州大学圣克鲁斯分校领导的团队将开发一种新方法，减少云和大气建模所需的计算机时间。该技术将能够以 3D 方式描述褐矮星和成像的系外行星大气层。这项技术将有助于研究云如何随着重要的大气特性（年龄、重力、温度和金属丰度）而变化。它还将有助于测试预测这些大气中天气的技术。因此，这项工作将增进对大气的理解。这项工作将为研究生和本科生提供支持，并将构成他们论文的基础。将创建实践活动和视频，让 K-12 学生熟悉大气层和编程。该提案的目标是实现褐矮星和成像系外行星大气层的 3D 表征。为了做到这一点，将创建一个替代辐射传输代码，该代码将利用神经网络的力量在当前代码所需时间的一小部分内对大气光谱进行建模。替代辐射传输代码将用于创建适合贝叶斯框架中时间分辨观测的 3D 映射代码。该代码将根据独立、最先进的全身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，可以对这些大气进行 3D 测绘，并且在未来几十年中，适当数据的数量将大幅增加。该代码将应用于现有观测，以创建第一张褐矮星大气 3D 地图。拟议的工作将创建能够在未来几十年内独特地实现比较气候学的工具。替代辐射传输代码和 3D 测绘代码将向社区开源。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。