Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

CHRNS: CANDOR Autonomous and efficient data collection


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.

CANDOR Efficient Data Collection Scheme
Principle of a data acquisition framework that maximizes the information content of the experiment.


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.


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. 

Screenshot of NICE and Autorefl working together
Red Box 1: The NCNR instrument control program NICE is being controlled by the AutoRefl program and is measuring a reflectivity data point.Red Box 2: The AutoRefl measurement queue that optimizes the measurement. This is re-optimized when new data is available, and a new model fit has been performed. Several data points are predicted so as to always be measuring if the fitting process is slow in comparison to data collection. Red Box 3: The progress of the current fit of the model leads to the whole set of reflectivity curves depicted in Box 4.Red Box 4: Blue dots are the measured data points, the broad bands indicate uncertainty at the 68 and 95 percentiles. 
Credit: David Hoogerheide


AutoRefl complete
Advanced stage of a measurement using AutoRefl 
Created February 13, 2023, Updated May 6, 2024