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Reduced-order model to approximate response matrices for filter stack spectrometers.

C-S Wong1, S V Luedtke1, D P Broughton1

  • 1Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

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Summary
This summary is machine-generated.

A new reduced-order model rapidly calculates response matrices for filter stack spectrometers (FSSs). This enables modular matrix construction for efficient FSS design optimization using machine learning.

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Area of Science:

  • Spectroscopy
  • Computational Physics
  • Materials Science

Background:

  • Filter stack spectrometers (FSSs) are crucial for various analytical applications.
  • Accurate response matrices are essential for FSS performance but computationally intensive to generate.
  • Optimizing FSS designs requires rapid evaluation of numerous configurations.

Purpose of the Study:

  • To introduce a reduced-order model (ROM) for accelerated response matrix calculation in FSSs.
  • To enable modular construction of response matrices from pre-computed physics data.
  • To provide a viable forward model for FSS design optimization, especially with machine learning.

Main Methods:

  • Developed a reduced-order modeling approach.
  • Implemented modular response matrix generation using pre-computed photon and electron transport/scattering data.
  • Integrated the ROM as a forward model for design optimization studies.

Main Results:

  • The ROM significantly speeds up response matrix computation for FSSs.
  • Modular matrices are suitable for design optimization, not high-fidelity data analysis.
  • The model facilitates rapid sampling and testing of diverse FSS designs.

Conclusions:

  • The reduced-order model offers an efficient method for FSS response matrix generation.
  • This approach is particularly beneficial for machine learning-driven FSS design optimization.
  • The modularity enhances flexibility in exploring various filter and detector materials.