Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics
Get on the BAND Wagon: a Bayesian framework for quantifying model uncertainties in nuclear dynamics
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DOI：
10.1088/1361-6471/abf1df
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
D. Phillips;R. Furnstahl;U. Heinz;T. Maiti;W. Nazarewicz;F. Nunes;M. Plumlee;M. Pratola;S. Pratt;F. Viens;Stefan M. Wild
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文献类型：
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作者：
D. Phillips;R. Furnstahl;U. Heinz;T. Maiti;W. Nazarewicz;F. Nunes;M. Plumlee;M. Pratola;S. Pratt;F. Viens;Stefan M. Wild
我们描述了核动力学贝叶斯分析（BAND）框架，我们正在开发的网络基础设施，将统一处理核模型，实验数据和相关的不确定性。我们概述了统计原理和核物理背景下的波段工具集，强调贝叶斯方法的能力，利用洞察力从多个模型。为了便于理解这些工具，我们提供了一个简单易用的BAND框架应用示例。四个案例研究，以突出该框架的元素将如何使复杂的，广泛的核物理问题的进展。通过收集符号和术语，提供说明性的例子，并给出了相关技术的概述，本文旨在开辟道路，通过核物理和统计社区可以促进和建立在BAND框架。
We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodology's ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND framework's application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.