Bayesian optimization in ab initio nuclear physics
| العنوان: | Bayesian optimization in ab initio nuclear physics |
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| المؤلفون: | Azam Sheikh Muhammad, Christian Forssén, Andreas Ekström, Devdatt Dubhashi, Christos Dimitrakakis, Alexander Schliep, Håkan T Johansson, Hans Salomonsson |
| المصدر: | Journal of Physics G: Nuclear and Particle Physics |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Physics, Coupling constant, FOS: Computer and information sciences, Nuclear and High Energy Physics, Computer Science - Machine Learning, Nuclear Theory, 010308 nuclear & particles physics, Scattering, Calibration (statistics), Bayesian optimization, Ab initio, FOS: Physical sciences, Machine Learning (stat.ML), 01 natural sciences, Domain (mathematical analysis), Machine Learning (cs.LG), Set (abstract data type), Nuclear Theory (nucl-th), Statistics - Machine Learning, 0103 physical sciences, Effective field theory, Statistical physics, 010306 general physics |
| الوصف: | Theoretical models of the strong nuclear interaction contain unknown coupling constants (parameters) that must be determined using a pool of calibration data. In cases where the models are complex, leading to time consuming calculations, it is particularly challenging to systematically search the corresponding parameter domain for the best fit to the data. In this paper, we explore the prospect of applying Bayesian optimization to constrain the coupling constants in chiral effective field theory descriptions of the nuclear interaction. We find that Bayesian optimization performs rather well with low-dimensional parameter domains and foresee that it can be particularly useful for optimization of a smaller set of coupling constants. A specific example could be the determination of leading three-nucleon forces using data from finite nuclei or three-nucleon scattering experiments. 33 pages, 14 figures |
| اللغة: | English |
| تدمد: | 0954-3899 1402-4896 1748-0221 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec41fc63fd636a0a4617d0784d2e056c http://arxiv.org/abs/1902.00941 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....ec41fc63fd636a0a4617d0784d2e056c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 09543899 14024896 17480221 |
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