Speaker
Description
Data from virtual experiments are becoming a valuable asset for research infrastructures: to develop and optimize current and future instruments; to train in the usage of the instrument control system; to study quantifying and reducing instrumental effects on acquired data. Furthermore large sets of simulated data are also a necessary ingredient for the development of surrogate models (supervised learning) for faster and more accurate simulation, reduction and analysis of the data.
So far, the production and usage of data from virtual experiments have been mostly reserved to simulation experts. With this work, data from virtual experiments are made available to the general users. The presented framework wraps in a digital twin of the facility instrument the knowledge of the physical description of the instrument, the simulation software and the high performing computing setup. The twin presented in this article has been developed at the ILL in the framework of the PANOSC European project in close collaboration with other research facilities (ESS and EuXFel) for some of its essential components. An overview of the core simulation software (McStas), its Python API (McStasScript), the public instrument description repository and the instrument control system (NOMAD) are given. The choices on the the communication patterns (based on ZQM) and interaction (via CAMEO) between the different components are also detailed. Example twins are also presented. The adoption of FAIR principles for data formats and policies in combination with open-source software make it a sustainable project both for development and maintenance in the mid and long-term.
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