Speaker
Description
An entire scientific workflow from acquisition through analysis has been automated, optimizing the success rate of measurements at the APS. Optimizations comprise leveraging a custom Python software stack integrating Bluesky (with connections to EPICS), the use of a large language model Scientific Companion, and the APS Data Management tools.
The integration simplifies the learning needed by new users and expands the capabilities previously available to existing beamlines. Further, the workflow facilitates rapid iteration through the use of pre-existing packages, as well as reducing software troubleshooting times.
By lowering the barrier of entry, streamlining the experience for new users, and expanding the existing capabilities for advanced beamline operations; this integration enables users to initiate bespoke experiment orchestration and data processing of scientific measurements.
This presentation will outline the structure of the generalized software stack and detail its application in enhancing diverse scientific techniques. We'll showcase how this strategy is applied to practical experiments through demonstrations in X-ray Photon Correlation Spectroscopy and High-energy Diffraction Microscopy. Beyond simplifying processes and improving efficiency, this work encourages a collaborative scientific environment by making advanced experimental techniques more accessible.
This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory and is based on research supported by the U.S. DOE Office of Science-Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
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