Speaker
Description
Automated analysis during synchrotron experiments has become crucial for modern research facilities. Rapid feedback from precise analytical results allows scientists to adjust measurement conditions, choose appropriate samples, and deploy adaptive strategies effectively, leading to optimized experiments and faster scientific breakthroughs.
The near real-time analysis pipeline presented here forms part of the ROCK-IT [1] project, which develops infrastructure for automated and remotely accessible in-situ and operando measurements. A key aspect of this project involves incorporating intelligent approaches to deliver data-driven experimental recommendations and notifications when experimental conditions are not met. The framework is designed to accommodate various potential functionalities such as energy drift forecasting in the absence of reference spectra, spectral noise reduction and signal reconstruction, anomaly detection, and intelligent determination of measurement completion. Access to analytical outcomes with minimum delay is essential to generate timely, evidence-based recommendations that improve both accuracy and operational speed throughout experiments, with some Machine Learning (ML) approaches currently under development.
We deployed an analysis framework at the Applied X-ray Absorption Spectroscopy beamline P65 located at PETRA III. The foundation of this system is Asapo [2], a data streaming framework developed at DESY to manage substantial data volumes with high efficiency. Building upon Asapo, we constructed a Python library that simplifies the creation of processing modules termed workers for individual pipeline operations. The implemented workers utilize the Larch [3] library to execute Calibration for the energy drift, Normalization and Linear Combination Analysis (LCA). This modular infrastructure scales to various experimental configurations with higher data rates and can integrate ML components that communicate with the control system or with the scientists to suggest dynamic modifications during ongoing measurements.
References
[1] https://www.rock-it-project.de/
[2] https://asapo.pages.desy.de/asapo/
[3] https://github.com/xraypy/xraylarch
