Mar 20 – 23, 2023
Campus Garching
Europe/Berlin timezone

AI-assisted neutron spectroscopy - Log-Gaussian processes for TAS

Mar 22, 2023, 12:10 PM
SCC/0-002 - Taurus 1&2 (Galileo)

SCC/0-002 - Taurus 1&2


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Talk (17 + 3 min) Neutron Instrumentation, Optics, Sample Environment, Detectors, and Software Data Evaluation & Software 2


Dr Mario Teixeira Parente (JCNS at MLZ, Forschungszentrum Jülich)


Three-axes spectroscopy (TAS) is a well-established method that has not substantially changed in the past decades of its use. Nowadays, with increasing demand and limited availability of TAS, application of AI methods is one option to increase their efficiency. From an AI perspective, TAS experiments collect noisy observations of a 2D intensity function to investigate a material of interest. If the intensity distribution is unknown, experimenters usually decide manually where to place measurements for a rapid overview. AI methods can assist this process by avoiding measurements in the background but preferring more informative regions of signal while taking instrument costs into account. Our method for discovering regions of signal is based on Gaussian Process Regression as a technique for probabilistic approximations of log-intensity functions. It handles noise and background and respects weak as well as strong intensities to avoid loss of information. For example, for simple dispersions like intensity-modulated phonons, full information can be achieved only within a reasonably short amount of experimental time. The algorithm was tested on simulated intensity functions (e.g., CEF, phonon, SDW) and experimentally on EIGER/PSI. In order to quantify the benefit of our approach, we present results of a benchmarking procedure that we have developed as a cost-benefit analysis in a synthetic but still representative setting.

Primary author

Dr Mario Teixeira Parente (JCNS at MLZ, Forschungszentrum Jülich)


Mr Georg Brandl (JCNS at MLZ, Forschungszentrum Jülich) Dr Christian Franz (JCNS at MLZ, Forschungszentrum Jülich) Dr Uwe Stuhr (Paul Scherrer Institute) Dr Marina Ganeva (JCNS at MLZ, Forschungszentrum Jülich) Dr Astrid Schneidewind (JCNS at MLZ, Forschungszentrum Jülich)

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