DS4MS

Data Science for Multiplexing Spectrometers

Started
November 10, 2022
Status
In Progress
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Abstract

The multiplexing neutron spectrometers at PSI enables to collect high-dimensional neutron scattering data. Existing methods do not utilize such datasets to their full potential, are time consuming, and require significant expert input. The project’s goal is to develop a generic, automatized method of fitting a signal simulation directly to a high-dimensional dataset, even when the simulation is computationally expensive, and even in the presence of background for which no analytic model exists. This data analysis tool will furthermore improve the data collection process during experiments through an automated intelligent decision making algorithm that will determine when a sufficient amount of neutrons has been collected in an arbitrary measurement setting. This work will enable research on new specific phenomena, free up time for instrument scientists, and ensure the optimal use of limited beam time.

People

Collaborators

SDSC Team:
Victor Cohen
Benjamin Béjar Haro

PI | Partners:

PSI, Laboratory for Neutron Scattering and Imaging:

  • Dr. Daniel Mazzone
  • Dr. Jakob Lass

More info

description

Motivation

The goal of the project is to develop a generic, automized method of fitting a signal of neutron scattering data in the presence of background noise. The first goal is to capture separately the signal and the background noise. We then aim at improving the data collection process through an intelligent decision making algorithm that will determine when a sufficient amount of neutrons has been collected in an arbitrary measurement setting.

Proposed Approach / Solution

SDSC develops ML models and optimization algorithms to tackle the fitting problem and the decision-making problem. First, the approach provides a denoising algorithm that extracts the signal from the noisy observations (Fig. 2). The solution is based on the resolution of a regularized problem that leverages the rotation invariance property of the background noise. Second, the approach identifies the regions that require more measurement to get a good signal-to-noise ratio. We create a stoping criterion ensuring that enough have been collected to get a good signal-to-noise ratio. The solution is implemented in the MJOLNIR software that treats the collected data.

Impact

The outcomes of the project will help to exploit collected data and further improve the use of the multiplexing instrument (Fig. 1) by reducing beam time and by providing more insights in the scattering data analysis. This approach could be also adapted for different application including time-of-light neutron spectroscopy.

Figure 1: Neutron scattering experiment at PSI. a) Multiplexing spectrometer b) Neutron detection over a wide range of angles.
Figure 2: Signal denoising on acquired data. a) Noisy observation plotted as function of energy and magnetic moment Q b) Denoised signal plotted as function of energy and magnetic moment Q.

Gallery

Annexe

Additional resources

Bibliography

  1. Lass, J., et al. Design and performance of the multiplexing spectrometer CAMEA.
  2. arXiv:2007.14796 [physics.ins-det]
  3. Allenspach, S., et al. (2021). Revealing three-dimensional quantum criticality by Sr
  4. substitution in Han purple. Physical Review Research, 3, 023177.
  5. Lass, J., Jacobsen, H., Mazzone, D. G., & Lefmann, K. (2020). MJOLNIR: a software package for multiplexing neutron spectrometers. SoftwareX, 12, 100600.
  6. Noack, M. et al. (2021). Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities. Nature Reviews Physics, 3, 685.

Publications

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