Nuclear mass predictions with machine learning reaching the accuracy required by astrophysical <em>r</em>-process studies

Presentation information

Contributed talks (Particle physics/Nuclear physics)

Particle physics/Nuclear physics

Sat. Aug 5, 2023 2:00 PM - 3:20 PM 502 (Room 4) (5F)

Chair: Shoji Hashimoto (High Energy Accelerator Research Organization (KEK))

2:20 PM - 2:40 PM

*Haozhao Liang¹, Zhongming Niu² (1. Tokyo Univ. (Japan), 2. Anhui Univ. (China))

Keywords:R-process nucleosynthesis, Nuclear mass, Bayesian neural network

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