The authors propose an alternative route to circumvent the limitation of neutron flux using the recent deep learning super-resolution technique. The feasibility of accelerating data collection has been demonstrated by using small-angle neutron scattering (SANS) data collected from the EQ-SANS instrument at Spallation Neutron Source (SNS). Data collection time can be reduced by increasing the size of binning of the detector pixels at the sacrifice of resolution. High-resolution scattering data is then reconstructed by using a deep learning-based super-resolution method. This will allow users to make critical decisions at a much earlier stage of data collection, which can accelerate the overall experimental workflow.

中文翻译：

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基于深度学习的小角度中子散射数据超分辨率：尝试加速实验工作流程

作者提出了使用最近的深度学习超分辨率技术来规避中子通量限制的替代途径。使用从散裂中子源 (SNS) 的 EQ-SANS 仪器收集的小角度中子散射 (SANS) 数据证明了加速数据收集的可行性。通过以牺牲分辨率为代价增加检测器像素的分箱大小，可以减少数据收集时间。然后使用基于深度学习的超分辨率方法重建高分辨率散射数据。这将允许用户在数据收集的更早阶段做出关键决策，从而加快整个实验工作流程。