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：在三轴谱仪（TAS）上进行的中子散射实验通过测量强度分布来研究磁激发和晶格激发，以理解材料特性的起源。然而，TAS实验的高需求与有限的束流时间自然引发了一个问题：我们能否提高实验效率并更好地利用实验者的时间？事实上，许多科学问题需要搜索信号，而若采用手动方式在非信息区域进行测量，可能耗时且低效。本文描述了一种概率性主动学习方法，该方法不仅能自主运行（即无需人为干预），还可通过利用log-Gaussian过程，以数学严谨且方法论稳健的方式直接提供信息测量的位置。最终，该方法的优势通过一个真实的TAS实验以及包含多种不同激发的基准测试得到了验证。