低剂量照射结构恢复的进展：混合分子吸附、对称性利用和最小信号水平的重建
Progress in structure recovery from low dose exposures: Mixed molecular adsorption, exploitation of symmetry and reconstruction from the minimum signal level
摘要：
我们调查的恢复结构从大面积，低剂量的曝光，分布在许多相同的副本的一个对象的剂量。重建是通过极大似然方法完成的，该方法既不需要识别也不需要对齐单个粒子。我们还模拟了石墨烯上的小分子吸附物，并演示了从\add{large area and}极低剂量原始数据中检索原子分辨率的图像。如果利用支持膜的所有对称性（平移、旋转和镜像）来恢复单个吸附分子的结构，则低至5 $e^{-}/$/$^{2}$的剂量就足够了。我们比较了不同的优化方案，考虑了混合分子和吸附位点，以及对数据量的要求。我们进一步证明了极大似然方法只需要每个实体至少有三个独立的计数，最后，我们证明了该方法与真实的实验数据和畸变的存在。
英文摘要：
We investigate the recovery of structures from large-area, low dose exposures that distribute the dose over many identical copies of an object. The reconstruction is done via a maximum likelihood approach that does neither require to identify nor align the individual particles. We also simulate small molecular adsorbates on graphene and demonstrate the retrieval of images with atomic resolution from \add{large area and} extremely low dose raw data. Doses as low as 5 $e^{-}/$Å$^{2}$ are sufficient if all symmetries (translations, rotations and mirrors) of the supporting membrane are exploited to retrieve the structure of individual adsorbed molecules. We compare different optimization schemes, consider mixed molecules and adsorption sites, and requirements on the amount of data. We further demonstrate that the maximum likelihood approach is only count limited by requiring at least three independent counts per entity.Finally, we demonstrate that the approach works with real experimental data and in presence of aberrations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
