Frameworks: Bayesian Analysis of Nuclear Dynamics

核物理学家寻求对原子核的性质、原子核之间的碰撞和极端环境的精确描述，比如宇宙形成的最初几秒钟或中子星的内部。这些情况涉及许多粒子通过复杂的力相互作用。它们每一个都有许多不同的模型来描述，这些模型通常可以准确地解释现有实验的结果。这些模型不能很好地预测未来实验中会发生什么，或者在地球上无法进入的环境中会发生什么。核动力学贝叶斯分析（BAND）框架将使用先进的统计方法，以最佳方式结合核物理模型，对尚未探索的情况进行预测。这些将比任何单个模型的预测更可靠。BAND的前沿计算机代码将广泛提供，并将促进核物理实验的设计，从而在理解方面获得最大的收获。在处理“模型不确定性”的其他科学中采用BAND的工具可能会刺激广泛的科学创新。参与BAND项目的本科生和研究生将获得数据科学、机器学习、核物理和高性能计算方面的广泛技术技能。核物理学家寻求对强相互作用物质的定量描述。中子和质子如何在原子核中相互作用的复杂模型、极端环境和原子核之间的碰撞是实现这一目标取得巨大进展的关键。这些模型通常很好地描述了现有的数据，但往往对未来的实验产生不同的预测。核动力学贝叶斯分析（BAND）框架将是一套广泛可用的计算工具，通过统计学家、计算机科学家和核物理学家之间的密切合作而建立。它将结合几个模型的结果，结合每个模型的先验知识和实验数据，对核物理预测的不确定性进行全面评估。这将有助于对未来实验的影响进行定量评估，加速理论-实验反馈循环，刺激创新。它还将有助于量化地球上无法到达的环境的不确定性，比如中子星的核心或大爆炸后的第一微秒。在其他科学领域，研究人员也面临着类似的挑战，因此BAND的工具将具有广泛的吸引力。参与BAND项目的本科生和研究生将获得统计方法和核物理方面的专业知识，以及大规模计算和机器学习方面的经验。该奖项由先进网络基础设施办公室颁发，由物理系信息前沿物理学和数学与物理科学理事会数学科学部CDS&amp；E项目共同支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Nuclear physicists seek an accurate description of the properties of atomic nuclei, collisions between nuclei, and extreme environments such as the first few seconds of our universe or the interior of a neutron star. These situations involve many particles interacting through complex forces. They’re each described by a number of different models that typically explain accurately results of existing experiments. The models don’t do as well predicting what will happen in future experiments or in environments that are inaccessible here on Earth. The Bayesian Analysis of Nuclear Dynamics (BAND) Framework will use advanced statistical methods to produce forecasts for as-yet-unexplored situations that combine nuclear-physics models in an optimal way. These will be more reliable than the predictions of any individual model. BAND’s forefront computer codes will be widely available and will facilitate the design of nuclear-physics experiments that can deliver the largest gain in understanding. The adoption of BAND’s tools in other sciences dealing with “model uncertainty” could spur broad scientific innovation. Undergraduate and graduate students working on BAND will gain a broad range of technical skills in data science, machine learning, nuclear physics, and high-performance computing.Nuclear physicists seek a quantitative description of strongly-interacting matter. Sophisticated models of how neutrons and protons interact in the nucleus, extreme environments, and collisions between nuclei have been key to the great progress made towards this goal. These models typically describe extant data well, but often yield divergent predictions for future experiments. The Bayesian Analysis of Nuclear Dynamics (BAND) framework will be a broadly available set of computational tools built through intensive collaboration between statisticians, computer scientists and nuclear physicists. It will combine the results of several models, incorporating prior knowledge and experimental data for each, to produce a full assessment of the uncertainty in nuclear-physics predictions. This will enable quantitative evaluation of the impact of future experiments, accelerating the theory-experiment feedback loop and spurring innovation. It will also help quantify uncertainties for terrestrially inaccessible environments, such as the core of neutron stars or the first microsecond after the Big Bang. Similar challenges are faced by researchers modeling complex dynamics in other sciences, so BAND’s tools will have broad appeal. Undergraduate and graduate students working on BAND will gain expertise in statistical methods and nuclear physics, as well as experience with large-scale computing and machine learning.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier in the Division of Physics and the CDS&E program in the Division of Mathematical Sciences within the Directorate for Mathematical and Physical 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.