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

C-S Wong; S V Luedtke; D P Broughton; J Strehlow; M Alvarado Alvarez; A Bogale; C-K Huang; B Wolfe; T R Schmidt; R E Reinovsky; B J Albright; S H Batha; S Palaniyappan

doi:10.1063/5.0218384

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

Rev Sci Instrum. 2024 Aug 1;95(8):083303. doi: 10.1063/5.0218384.

Authors

C-S Wong¹, S V Luedtke¹, D P Broughton¹, J Strehlow¹, M Alvarado Alvarez¹, A Bogale^{1

2}, C-K Huang¹, B Wolfe¹, T R Schmidt¹, R E Reinovsky¹, B J Albright¹, S H Batha¹, S Palaniyappan¹

Affiliations

¹ Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
² Center for Energy Research, University of California-San Diego, La Jolla, California 92093, USA.

PMID: 39115399
DOI: 10.1063/5.0218384

Abstract

We present a reduced-order model to calculate response matrices rapidly for filter stack spectrometers (FSSs). The reduced-order model allows response matrices to be built modularly from a set of pre-computed photon and electron transport and scattering calculations through various filter and detector materials. While these modular response matrices are not appropriate for high-fidelity analysis of experimental data, they encode sufficient physics to be used as a forward model in design optimization studies of FSSs, particularly for machine learning approaches that require sampling and testing a large number of FSS designs.

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