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English(EN) Posterior Inference of Hamiltonian Parameters from RIXS Spectroscopy

新AI方法从RIXS光谱数据中推断哈密顿量参数

研究人员应用了基于仿真的推理,利用视觉Transformer编码器来分析共振非弹性X射线散射(RIXS)光谱数据。这种新颖的方法有效地约束了先验并估计了联合密度,从而能够推断Ni$^{2+}$化合物NiPS$_3$和K$_2$NiF$_4$的哈密顿量参数的完整后验分布。该方法成功地恢复了参数相关性,并准确匹配了观测到的光谱,解锁了新的分析能力,如干扰项边缘化不确定性量化和多测量后验融合。 AI

影响 这项研究展示了AI技术在推进凝聚态物理科学发现方面的新颖应用。

排序理由 该条目是一篇arXiv预印本,详细介绍了一种新的科学数据分析方法。[lever_c_demoted from research: ic=1 ai=1.0]

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新AI方法从RIXS光谱数据中推断哈密顿量参数

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该条目是一篇arXiv预印本,详细介绍了一种新的科学数据分析方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Samuel Klein, Thomas M. Linker, Louis Conreux, Daniel Ratner, Apurva Mehta, Makoto Tachibana, Jiemin Li, Jonathan Pelliciari, Valentina Bisogni, Wei He, Xiangpeng Luo, Mark P. M. Dean, Marton K. Lajer, Michael Kagan, Joshua J. Turner, Yongqiang Cheng, Se… ·

    从RIXS光谱中对哈密顿参数进行后验推断

    arXiv:2608.13848v1 Announce Type: cross Abstract: We present the first application of simulation-based inference to resonant inelastic X-ray scattering spectroscopy. Using truncated marginal neural ratio estimation to efficiently restrict the prior and conditional flow matching a…