To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produce...To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.展开更多
近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果...近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。展开更多
基金National Natural Science Foundation of China(12125509,11961141003,12275361,U2267205,12175152,12175121)National Key Research and Development Project(2022YFA1602301)Continuous-support Basic Scientific Research Project。
文摘To generate a neutron beam exhibiting a Maxwellian energy distribution with narrow emission angles for measuring the neutron capture reaction rates of the s-process nuclides,a monoenergetic 3.4 MeV proton beam produced by the tandem-accelerator in the China Institute of Atomic Energy was utilized.The proton beam was first transmitted through a 60.5μm aluminum foil and then impinged on a natural LiF target to produce neutron beam via^(7)Li(p,n)7Be reaction.The quasi-Gaussian energy distribution of protons in the LiF target resulted in neutron energy spectra that agreed with a Maxwellian energy distribution at kT=(22±2)keV,which was achieved by integrating neutrons detected within an emission angle of 65.0°±2.6°using a ^(6)Li glass detector positioned at 65°relative to the proton beam direction.The narrow angular spread of the Maxwelliandistributed neutron beam enables direct measurement of neutron capture cross-sections for most s-process nuclides,overcoming previous experimental limitations associated with broad angular distributions.
文摘近期提出的单体相移深度神经网络(single phase-shift deep neural network,SPDNN),因其网络规模小、学习精度高,成为首个复杂中子共振截面拟合与评价的实用深度学习工具。在SPDNN学习共振截面的过程中,诸多因素显著影响网络的训练效果、训练效率以及训练模型的泛化性。这些因素包括:决定网络相移层大小的共振截面频谱范围与频段宽度、隐藏层的数目、每层神经元的数目、激活函数、损失函数、训练步数和训练数据的预处理等。为了进一步提升SPDNN在共振截面研究中的实用性,详细考察了这些因素对网络拟合性能的影响。通过考察,确定了SPDNN在共振截面研究中适宜的网络构建和训练方法,助力推动SPDNN的广泛应用。
