Advances in utilizing event based data structures for neutron scattering experiments

Area of Science:

• Neutron scattering
• Materials science
• Data science

Background:

• Spallation neutron sources widely use event recording in data acquisition systems.
• Previous data processing methods (histograms) limited temporal or file-based analysis.
• Event mode data collection offers new avenues for scientific discovery.

Purpose of the Study:

• Demonstrate advancements in data processing using event mode measurements.
• Explore new scientific possibilities enabled by event mode data.
• Highlight techniques for analyzing time-dependent data and improving measurement strategies.

Main Methods:

• Review principles of event data acquisition and processing.
• Introduce techniques for exploring temporal changes in event mode data.
• Apply hierarchical clustering for model-independent data grouping and analysis.

Main Results:

• Event mode data allows for post-measurement data exploration and rebinning.
• Statistical methods for determining data quality and optimal measurement duration are demonstrated.
• Hierarchical clustering effectively improves calibration, reduction, and data exploration.

Conclusions:

• Event mode data processing significantly enhances the capabilities of spallation neutron sources.
• This approach unlocks new possibilities for in situ measurements and detailed data analysis.
• Advanced data analysis techniques, like hierarchical clustering, are crucial for maximizing scientific return.