Frameworks: Bayesian Analysis of Nuclear Dynamics

框架：核动力学贝叶斯分析

• 批准号: 2004601
• 负责人: Daniel Phillips
• 金额: $ 371.66万
• 依托单位: Ohio University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004601&HistoricalAwards=false
• 关键词: Frameworks; Bayesian; Analysis; Nuclear; Dynamics

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.

核物理学家寻求精确描述原子核的性质，原子核之间的碰撞，以及极端环境，如我们宇宙的最初几秒钟或中子星星的内部。这些情况涉及许多粒子通过复杂的力相互作用。它们每个都由许多不同的模型描述，这些模型通常可以准确解释现有实验的结果。这些模型不能很好地预测未来的实验或地球上无法到达的环境中会发生什么。核动力学贝叶斯分析框架将使用先进的统计方法，以最佳方式结合联合收割机核物理模型，对尚未探索的情况进行预测。这将比任何单个模型的预测更可靠。BAND的前沿计算机代码将被广泛使用，并将促进核物理实验的设计，以提供最大的理解增益。在处理“模型不确定性”的其他科学中采用BAND的工具可以刺激广泛的科学创新。在BAND工作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。核物理学家寻求强相互作用物质的定量描述。关于中子和质子如何在原子核中相互作用、极端环境以及原子核之间碰撞的复杂模型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，但往往会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建立的一套广泛可用的计算工具。它将联合收割机结合几个模型的结果，并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。这将能够对未来实验的影响进行定量评估，加速理论-实验反馈回路并刺激创新。它还将有助于量化地球无法到达的环境的不确定性，例如中子星的核心或大爆炸后的第一微秒。在其他科学中，研究人员在模拟复杂动力学时也面临着类似的挑战，因此BAND的工具将具有广泛的吸引力。在BAND工作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的经验。高级网络基础设施办公室的这一奖项由物理学部信息前沿物理学和&数学和物理科学局数学科学部CDS E计划共同支持。这一奖项反映了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。

项目成果

Investigation of direct capture in the Na23(p,γ)Mg24 reaction

Na23(p,γ)Mg24 反应中直接捕获的研究

• DOI: 10.1103/physrevc.106.045801
• 发表时间: 2022
• 期刊: Physical Review C
• 影响因子: 3.1
• 作者: Boeltzig, A.;deBoer, R. J.;Chen, Y.;Best, A.;Couder, M.;Di Leva, A.;Frentz, B.;Görres, J.;Gyürky, Gy.;Imbriani, G.
• 通讯作者: Imbriani, G.

Fast emulation of quantum three-body scattering

量子三体散射的快速仿真

• DOI: 10.1103/physrevc.105.064004
• 发表时间: 2022
• 期刊: Physical Review C
• 影响因子: 3.1
• 作者: Zhang, Xilin;Furnstahl, R. J.
• 通讯作者: Furnstahl, R. J.

Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables

• DOI: 10.1103/physrevc.104.064001
• 发表时间: 2021-04
• 期刊: Physical Review C
• 影响因子: 3.1
• 作者: S. Wesolowski;I. Svensson;A. Ekström;C. Forssén;R. Furnstahl;J. Melendez;D. Phillips

Machine-learning-based inversion of nuclear responses

• DOI: 10.1103/physrevc.103.035502
• 发表时间: 2020-10
• 期刊: Physical Review C
• 影响因子: 3.1
• 作者: Krishnan Raghavan;Prasanna Balaprakash;A. Lovato;N. Rocco;Stefan M. Wild

Precision mass measurement of lightweight self-conjugate nucleus 80Zr

• DOI: 10.1038/s41567-021-01395-w
• 发表时间: 2021-11
• 期刊: Nature Physics
• 影响因子: 19.6
• 作者: A. Hamaker;E. Leistenschneider;R. Jain;G. Bollen;S. Giuliani;K. Lund;W. Nazarewicz;L. Neufcourt