Journal Article

FZJ-2022-01283

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Benchmarking Autonomous Scattering Experiments Illustrated on TAS

Teixeira Parente, M. (Corresponding author)FZJ* ; Schneidewind, A.FZJ* ; Brandl, G.FZJ* ; Franz, C.FZJ* ; Noack, M. ; Boehm, M. ; Ganeva, M. (Last author)FZJ*

2022
Frontiers Media

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Please use a persistent id in citations: http://hdl.handle.net/2128/30752  doi:10.3389/fmats.2021.772014

Abstract: With the advancement of artificial intelligence and machine learning methods, autonomous approaches are recognized to have great potential for performing more efficient scattering experiments. In our view, it is crucial for such approaches to provide thorough evidence about respective performance improvements in order to increase acceptance within a scientific community. Therefore, we propose a benchmarking procedure designed as a cost-benefit analysis that is applicable to any scattering method sequentially collecting data during an experiment. For a given approach, the performance assessment is based on how much benefit, given a certain cost budget, it is able to acquire in predefined test cases. Different approaches thus get a chance for comparison and can make their advantages explicit and visible. Key components of the procedure, i.e., cost measures, benefit measures, and test cases, are made precise for the setting of three-axes spectrometry (TAS) as an illustration. Finally, we discuss neglected aspects and possible extensions for the TAS setting and comment on the procedure’s applicability to other scattering methods. A Python implementation of the procedure to simplify its utilization by interested researchers from the field is also provided.

Keyword(s): Instrument and Method Development (1st) ; Instrument and Method Development (2nd)

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Contributing Institute(s):

1. JCNS-FRM-II (JCNS-FRM-II)
2. Heinz Maier-Leibnitz Zentrum (MLZ)
3. Streumethoden (JCNS-2)
4. JCNS-4 (JCNS-4)

Research Program(s):

1. 6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ) (POF4-6G4) (POF4-6G4)
2. 623 - Data Management and Analysis (POF4-623) (POF4-623)

Experiment(s):

1. PANDA: Cold three axes spectrometer (SR2)

Appears in the scientific report 2022

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