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

宇宙微波背景辐射（CMB）是现代宇宙学的支柱之一-它证实了大爆炸的标准理论，并有助于揭示宇宙的结构和内容。该项目研究了CMB的下一个重大潜在发现，即原始重力波（PGW）的探测。然而，污染物对CMB信号的掩蔽对高精度测量是有害的，特别是对预期的微弱PGW信号是有害的。从CMB-Stage-4等实验预期的大量数据中提取和分析CMB信号提出了需要复杂的新方法的挑战。德克萨斯大学达拉斯的计算机科学家和天体物理学家的跨学科团队将为此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，以产生强大而有效的模型，这些模型结合了领域专业知识并尊重已知的物理约束。该团队将通过推广工作来补充这项研究，以促进和增加达拉斯-沃思堡（DFW）地区公众对科学和技术的参与，包括（1）在高中教师会议Mini-CAST上组织宇宙学和ML年度研讨会，该会议隶属于德克萨斯州科学教师协会，以及（2）积极参与低社会经济社区以及更广泛的DFW地区的科学营和交流，以接触并招募STEM领域代表性不足的群体的学生。这个ADAPT RAISE项目包括天体物理学家和计算机科学家的专业知识和共同努力的融合，超越了学科的简单组合，旨在改造每个学科，以提供一个快速和科学的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中产生CMB清洁的温度和偏振功率谱。这是因为人们认识到，将ML应用于CMB不应该试图复制传统方法的处理步骤，而应该充分利用ML的优势-从数据中提取丰富的模式。其次，ML方法构建了DNN，通过统计模型将软科学领域知识纳入其中，以规范和通知模型。这种方法的直接结果是CMB功率谱谐波分量可以用于ML损失函数中，允许在模型训练期间充分利用其物理和数学特性。虽然这项研究的重点是开发和应用DNN ML方法到CMB，这里开发的工具和方法在科学中有深远的应用。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.