Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and lattice excitations by measuring intensity distributions to understand the origins of materials properties. The high demand and limited availability of beam time for TAS experiments however raise the natural question whether we can improve their efficiency and make better use of the experimenter's time. In fact, there are a number of scientific problems that require searching for signals, which may be time consuming and inefficient if done manually due to measurements in uninformative regions. Here, we describe a probabilistic active learning approach that not only runs autonomously, i.e., without human interference, but can also directly provide locations for informative measurements in a mathematically sound and methodologically robust way by exploiting log-Gaussian processes. Ultimately, the resulting benefits can be demonstrated on a real TAS experiment and a benchmark including numerous different excitations.
翻译:在三轴光谱仪上进行的中子散射实验通过测量强度分布来研究磁性和晶格激发,以理解材料特性的起因。高需求和有限的光束时间导致自然引出一个问题,即能否改善其效率并更好地利用实验者的时间。事实上,有一些科学问题需要搜索信号,如果在不具备信息的区域手动进行测量,这可能是耗时且低效的。在这里,我们描述了一种概率主动学习方法,它不仅可以在数学上和方法论上健壮地利用对数高斯过程直接提供有信息量的测量位置,而且还可以自主运行,即不需要人类干预。最终的效益可以在真实的三轴光谱仪实验和基准测试中体现,包括多种不同的激发。