Dilute neutron star matter from neural-network quantum states
Dilute neutron star matter from neural-network quantum states
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DOI：
10.1103/physrevresearch.5.033062
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发表时间：
2022-12
影响因子：
4.2
通讯作者：
Bryce Fore;Jane M. Kim;Giuseppe Carleo;M. Hjorth-Jensen;A. Lovato;M. Piarulli
中科院分区：
文献类型：
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作者：
Bryce Fore;Jane M. Kim;Giuseppe Carleo;M. Hjorth-Jensen;A. Lovato;M. Piarulli
低密度中子物质具有引人入胜的浮现量子现象，例如库珀对的形成和超流的开始。我们利用隐核子神经网络量子态的表现性，结合变分蒙特卡罗和随机重构技术来模拟这种密度机制。我们的方法与辅助场扩散蒙特卡罗方法相比，计算量只有一小部分。利用前级无标度有效场论哈密顿量，计算了无限大中子物质的每粒子能量，并与从高度真实的相互作用中得到的结果进行了比较。此外，自旋单态和三态两体分布函数的比较表明，在$^1S_0通道中出现了配对现象。
Low-density neutron matter is characterized by fascinating emergent quantum phenomena, such as the formation of Cooper pairs and the onset of superfluidity. We model this density regime by capitalizing on the expressivity of the hidden-nucleon neural-network quantum states combined with variational Monte Carlo and stochastic reconfiguration techniques. Our approach is competitive with the auxiliary-field diffusion Monte Carlo method at a fraction of the computational cost. Using a leading-order pionless effective field theory Hamiltonian, we compute the energy per particle of infinite neutron matter and compare it with those obtained from highly realistic interactions. In addition, a comparison between the spin-singlet and triplet two-body distribution functions indicates the emergence pairing in the $^1S_0$ channel.