RAISE: ADAPT : Novel AI/ML methods to derive CMB temperature and polarization power spectra from uncleaned maps

RAISE：ADAPT：从未清理的地图中导出 CMB 温度和偏振功率谱的新颖 AI/ML 方法

• 批准号: 2327245
• 负责人: Mustapha Ishak-Boushaki
• 金额: $ 62.21万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327245&HistoricalAwards=false
• 关键词: RAISE; ADAPT; Novel; AI; ML

Cosmic Microwave Background (CMB) radiation is one of the pillars of modern cosmology – it confirmed the standard theory of the Big Bang and helped reveal the structure and content of the universe. This project investigates the next major potential discovery from the CMB, the detection of primordial gravity waves (PGW). However, the masking of the CMB signal by contaminants is detrimental to high-precision measurements and in particular to the expected faint PGW signal. Extracting and analyzing the CMB signal from the overwhelming amounts of data expected from experiments such as CMB-Stage-4 presents challenges that require sophisticated new methods. The interdisciplinary team of computer scientists and astrophysicists at the University of Texas, Dallas, will develop and apply novel Machine Learning (ML) methods for this endeavor. Their methodology combines the predictive power of modern Deep Neural Networks (DNNs) with statistical tools to produce powerful and efficient models that incorporate domain expertise and respect known physical constraints. The team will complement this research with outreach efforts to promote and increase public engagement with science and technology within the Dallas-Fort Worth (DFW) area, including (1) organizing yearly workshops for cosmology and ML at the high-school teacher conference Mini-CAST, which is affiliated with the Science Teachers Association of Texas, and (2) actively participating in science camps and exchanges in low-socioeconomic communities as well as the broader DFW area to reach out and recruit students from underrepresented groups in STEM fields. This ADAPT RAISE project includes an amalgamation of expertise and joint efforts from astrophysicists and computer scientists that goes beyond a simple combination of the subjects and aims to transform each of them to provide a fast and scientifically informed ML model to deal with CMB contaminants and analysis. The team will first develop a novel method that produces CMB clean temperature and polarization power spectra directly from uncleaned maps. This comes from the realization that application of ML to CMB should not try to replicate the processing steps of traditional methods but rather take full advantage of what ML is exactly good at – extracting rich patterns from data. Second, the ML approach builds DNNs that incorporate soft scientific domain knowledge via statistical models to regularize and inform the model. An immediate consequence of this approach is that the CMB power spectra harmonic components can be used in the ML loss function allowing one to take full advantage of their physical and mathematical properties during the model training. While this investigation is focused on developing and applying DNN ML methods to the CMB, the tools and approaches developed here have far-reaching applications in sciences.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.

宇宙微波背景辐射是现代宇宙学的支柱之一，它证实了大爆炸的标准理论，并帮助揭示了宇宙的结构和内容。该项目调查了CMB的下一个重大潜在发现--原始重力波(PGW)的探测。然而，污染物对CMB信号的掩蔽不利于高精度测量，特别是对预期的微弱PGW信号不利。从CMB-Stage-4等实验预期的海量数据中提取和分析CMB信号提出了挑战，需要复杂的新方法。德克萨斯大学达拉斯分校的计算机科学家和天体物理学家组成的跨学科团队将为这一努力开发和应用新的机器学习(ML)方法。他们的方法将现代深度神经网络(DNN)的预测能力与统计工具相结合，以产生功能强大且高效的模型，这些模型结合了领域专业知识并考虑了已知的物理约束。该团队将通过外联活动补充这项研究，以促进和增加公众对达拉斯-沃斯堡(DFW)地区科学技术的参与，包括(1)在隶属于德克萨斯州科学教师协会的高中教师会议Mini-Cast上组织宇宙学和ML年度研讨会，以及(2)积极参加科学夏令营和低社会经济社区以及更广泛的DFW地区的交流，接触并招收STEM领域中代表性不足的群体的学生。这个Adapt Raise项目融合了天体物理学家和计算机科学家的专业知识和共同努力，超越了这些学科的简单组合，旨在改变它们中的每一个，以提供一个快速和科学知情的ML模型来处理CMB污染物和分析。该团队将首先开发一种新的方法，直接从未清洁的地图生成CMB清洁的温度和偏振功率谱。这是因为人们意识到，ML在CMB中的应用不应该试图重复传统方法的处理步骤，而是充分利用ML所擅长的-从数据中提取丰富的模式。其次，ML方法通过统计模型构建包含软科学领域知识的DNN，以规则化和告知模型。这种方法的直接结果是，可以将CMB功率谱谐波分量用于最大似然损失函数，从而在模型训练期间充分利用其物理和数学特性。虽然这项调查的重点是开发和应用DNN ML方法到CMB，但这里开发的工具和方法在科学上具有深远的应用。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。