Machine-learning-based inversion of nuclear responses
Machine-learning-based inversion of nuclear responses
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DOI：
10.1103/physrevc.103.035502
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发表时间：
2020-10
影响因子：
3.1
通讯作者：
Krishnan Raghavan;Prasanna Balaprakash;A. Lovato;N. Rocco;Stefan M. Wild
中科院分区：
文献类型：
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作者：
Krishnan Raghavan;Prasanna Balaprakash;A. Lovato;N. Rocco;Stefan M. Wild
原子核与外部电弱探针相互作用的微观描述是阐明短程核动力学和正确解释中微子振荡实验所必需的。核量子Monte Carlo方法从它们的拉普拉斯变换中推导出核的电弱响应函数。逆拉普拉斯变换是一个众所周知的不适定问题，贝叶斯技术，如最大熵，通常用于重建原始的响应函数在准弹性区域。在这项工作中，我们提出了一个物理通知人工神经网络架构适合近似逆的拉普拉斯变换。利用模拟的，虽然现实的，电磁响应函数，我们表明，这种物理信息的人工神经网络优于最大熵在低能量转移和准弹性区域，从而允许强大的计算电子散射和中微子散射的核和包容性μ子捕获率。
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments. Nuclear quantum Monte Carlo methods infer the nuclear electroweak response functions from their Laplace transforms. Inverting the Laplace transform is a notoriously ill-posed problem; and Bayesian techniques, such as maximum entropy, are typically used to reconstruct the original response functions in the quasielastic region. In this work, we present a physics-informed artificial neural network architecture suitable for approximating the inverse of the Laplace transform. Utilizing simulated, albeit realistic, electromagnetic response functions, we show that this physics-informed artificial neural network outperforms maximum entropy in both the low-energy transfer and the quasielastic regions, thereby allowing for robust calculations of electron scattering and neutrino scattering on nuclei and inclusive muon capture rates.