RUI: An Inference Methodology to Illuminate Nonlinear Neutrino Flavor Transformation for Nuclear Astrophysics

RUI：一种阐明核天体物理学非线性中微子味道变换的推理方法

• 批准号: 2310066
• 负责人: Eve Armstrong
• 金额: $ 36万
• 依托单位: New York Institute of Technology
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310066&HistoricalAwards=false
• 关键词: RUI; Inference; Methodology; Illuminate; Nonlinear

Probing the physics underlying cosmic explosions is vital for understanding the makeup of the observable Universe. The explosions of massive stars are candidate sites for the nucleosynthesis of some heavy elements – the building blocks of life on Earth. Important aspects of these explosions, however, are difficult to access via traditional approaches in nuclear astrophysics. This is due both to a lack of adaptability of existing codes to the required mathematical framework, and to computational expense. Moreover, important features of the physics remain artificially hidden from the tools built to describe them. Inference (related to the common term “machine learning”) is an alternative methodology. In the geosciences and neurobiology, inference has for decades illuminated problems akin to those that hinder progress within nuclear astrophysics. For that reason, recently inference has been brought into astrophysics, where proof-of-concept simulations have been successful. This project builds beyond those tests, integrating inference into larger-scale codes and handling real astrophysical data. Innovations cultivated within one scientific arena can be transformative when expanded for disjoint fields. Integral to the research is the training of undergraduates, many with socio-economic backgrounds under-represented in science. Students also engage in comedic science outreach, to build communication skills. The physics noted as “artificially hidden” from traditional techniques is direction-changing backscattering in the neutrino flavor field in these high-density environments. Neutrinos are elementary particles whose “flavor” dictates the manner in which they interact with other particles. Flavor in large part sets the neutron-to-proton ratio as well as energy and entropy deposition, thereby in-part dictating the mechanism of explosion and nucleosynthesis. Backscattering in the flavor field can significantly shape the explosion. But it presents a two-point boundary-value problem: a framework that traditional numerical integration is ill-equipped to handle. This project applies statistical data assimilation (SDA) to illuminate this problem. SDA is a Bayesian inference methodology, invented for numerical weather prediction, to predict sparsely-sampled nonlinear systems. SDA is well-suited for solving boundary-value problems, and it is expected to outperform integration in computational efficiency. This project builds upon previous work that established that SDA can 1) outperform integration in terms of solving a direction-changing backscattering problem, 2) search parameter space more efficiently than integration, and 3) find solutions to simple problems where the data are real, rather than simulated. These findings call for a deeper examination of SDA’s ability to solve more complex parameter estimation problems and augment larger-scale codes.This project advances the objectives of "Windows on the Universe: the Era of Multi-Messenger Astrophysics", one of the 10 Big Ideas for Future NSF Investments.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.

探索宇宙爆炸背后的物理学对于理解可观测宇宙的构成至关重要。 大质量恒星的爆炸是某些重元素核合成的候选地点--这些重元素是地球上生命的基石。 然而，这些爆炸的重要方面很难通过核天体物理学的传统方法获得。 这是由于现有的代码所需的数学框架缺乏适应性，并计算费用。 此外，物理学的重要特征仍然被人为地隐藏在描述它们的工具之外。 推理（与通用术语“机器学习”相关）是一种替代方法。 在地球科学和神经生物学中，几十年来，推理已经阐明了与那些阻碍核天体物理学进步的问题类似的问题。 由于这个原因，最近推断已被引入天体物理学，其中概念验证模拟已经成功。 该项目超越了这些测试，将推理集成到更大规模的代码中，并处理真实的天体物理数据。 在一个科学竞技场内培育的创新，如果扩展到不相交的领域，可能会带来变革。 研究的组成部分是对本科生的培训，其中许多人的社会经济背景在科学领域的代表性不足。 学生还从事喜剧科学推广，以建立沟通技巧。 在这些高密度环境中，中微子味场中的反向散射方向改变是传统技术中“人为隐藏”的物理现象。 中微子是基本粒子，其“味道”决定了它们与其他粒子相互作用的方式。 味道在很大程度上决定了中子与质子的比例以及能量和熵的沉积，从而在一定程度上决定了爆炸和核合成的机制。 味道场中的后向散射可以显著地塑造爆炸。 但它提出了一个两点边值问题：一个框架，传统的数值积分是装备不良处理。 本计画应用统计资料同化（SDA）来说明这个问题。 SDA是一种贝叶斯推理方法，发明用于数值天气预报，以预测稀疏采样的非线性系统。 SDA非常适合于求解边值问题，并且预计在计算效率上优于积分。 这个项目建立在以前的工作，建立SDA可以1）优于积分在解决方向变化的后向散射问题，2）搜索参数空间比积分更有效，3）找到解决简单问题的数据是真实的，而不是模拟。 这些发现要求对SDA解决更复杂的参数估计问题和增强更大规模代码的能力进行更深入的检查。多信使天体物理学时代”，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。