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American Physical Society, Physical Review Special Topics: Accelerators and Beams, 12(13), 2010

DOI: 10.1103/physrevstab.13.120403

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Characterization and error analysis of an N X N unfolding procedure applied to filtered, photoelectric x-ray detector arrays. II. Error analysis and generalization

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

A five-channel, filtered-x-ray-detector (XRD) array has been used to measure time-dependent, soft-x-ray flux emitted by z-pinch plasmas at the Z pulsed-power accelerator (Sandia National Laboratories, Albuquerque, New Mexico, USA). The preceding, companion paper [D. L. Fehl , Phys. Rev. ST Accel. Beams 13, 120402 (2010)PRABFM1098-4402] describes an algorithm for spectral reconstructions (unfolds) and spectrally integrated flux estimates from data obtained by this instrument. The unfolded spectrum Sunfold(E,t) is based on (N=5) first-order B-splines (histograms) in contiguous unfold bins j=1,...,N; the recovered x-ray flux Funfold(t) is estimated as ∫Sunfold(E,t)dE, where E is x-ray energy and t is time. This paper adds two major improvements to the preceding unfold analysis: (a) Error analysis.---Both data noise and response-function uncertainties are propagated into Sunfold(E,t) and Funfold(t). Noise factors nu are derived from simulations to quantify algorithm-induced changes in the noise-to-signal ratio (NSR) for Sunfold in each unfold bin j and for Funfold (nu≡NSRoutput/NSRinput): for Sunfold, 1≲nuj≲30, an outcome that is strongly spectrally dependent; for Funfold, 0.6≲nuF≲1, a result that is less spectrally sensitive and corroborated independently. For nominal z-pinch experiments, the combined uncertainty (noise and calibrations) in Funfold(t) at peak is estimated to be ˜15%. (b) Generalization of the unfold method.---Spectral sensitivities (called here passband functions) are constructed for Sunfold and Funfold. Predicting how the unfold algorithm reconstructs arbitrary spectra is thereby reduced to quadratures. These tools allow one to understand and quantitatively predict algorithmic distortions (including negative artifacts), to identify potentially troublesome spectra, and to design more useful response functions.