CHRNS: CANDOR Autonomous and efficient data collection

Background

In any experiment, some of the data collected are more useful for answering the scientific question at hand than the rest. If the useful data could be identified in advance, a significant amount of experiment time could be saved; unfortunately, the knowledge required for this identification is not gained until after the experiment is run. Active learning techniques can resolve this contradiction. These techniques operate cyclically, processing and analyzing experimental data and using the analysis in real-time to identify the most valuable data points to be collected next.

Neutron scattering techniques are inherently slow and are thus good candidates for active learning-based autonomous data collection. In addition, many neutron scattering techniques are fundamentally model-based. Examples include diffraction techniques, which measure shaped peaks in reciprocal space; neutron spin echo, which measures a damped sinusoid; or neutron reflectometry, for which data analysis relies on models of thin film structures. In each case, autonomous data collection can identify in real-time the data that will constrain the model parameters of interest in the least amount of measurement time.

Implementation

Using simulated experiments on the CANDOR reflectometer, we determined that 2x - 5x speed-ups are realistic. We successfully devised a framework for efficient reflectometry data collection (manuscript in preparation [1]) and have implemented software to achieve this. Since the framework will collect reflectometry data out-of-order compared to traditional trajectory-based data acquisition, fundamental changes to the instrument control software were required. Those changes are tightly coordinated with the non-equilibrium data acquisition and FAIR data efforts.

AutoRefl

The data being collected by a neutron reflectometer can be analyzed in real-time, and this information is used to predict which new data points will give the most information going forward. The new data points are then added to set a new measurement queue; once new data is analyzed the queue is again updated. 

We have tested the software using the new CHRNS computers, NICE, and a realistic implementation of the reflectometer, MAGIC. Note that we chose this instrument as it is easier to visualize the point-by-point data collection compared to the range of angles covered by the white-beam CANDOR reflectometer - but the process is identical. The software takes into account all needed factors such as background measurements, time to move the detector, deadtime, etc. Here is a screenshot of a simulation in progress.