TY  - JOUR
AU  - Teixeira Parente, Mario
AU  - Brandl, Georg
AU  - Franz, Christian
AU  - Stuhr, Uwe
AU  - Ganeva, Marina
AU  - Schneidewind, Astrid
TI  - Active learning-assisted neutron spectroscopy with log-Gaussian processes
JO  - Nature Communications
VL  - 14
SN  - 2041-1723
PB  - Nature Publishing Group
M1  - FZJ-2023-01907
SP  - 2246
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 37076453
UR  - <Go to ISI:>//WOS:000988360100015
DO  - DOI:10.1038/s41467-023-37418-8
UR  - https://juser.fz-juelich.de/record/1006875
ER  -