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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.
The Review of Scientific Instruments
|August 8, 2024
Summary
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.
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.
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