We present a novel methodology of augmenting the scattering data measured by small angle neutron scattering via an emerging deep convolutional neural network (CNN) that is widely used in artificial intelligence (AI). Data collection time is 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 AI deep super-resolution learning method. This technique can not only improve the productivity of neutron scattering instruments by speeding up the experimental workflow but also enable capturing kinetic changes and transient phenomenon of materials that are currently inaccessible by existing neutron scattering techniques.
@article{arxiv.1904.08450,
title = {Accelerating Neutron Scattering Data Collection and Experiments Using AI Deep Super-Resolution Learning},
author = {Ming-Ching Chang and Yi Wei and Wei-Ren Chen and Changwoo Do},
journal= {arXiv preprint arXiv:1904.08450},
year = {2019}
}