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 et al., Phys. Rev. ST Accel. Beams 13, 120402 (2010)] describes an algorithm for spectral reconstructions (unfolds) and spectrally integrated flux estimates from data obtained by this instrument. The unfolded spectrum ${S}_{\mathrm{unfold}}(E,t)$ is based on ($N=5$) first-order B-splines (histograms) in contiguous unfold bins $j=1,\dots{},N$; the recovered x-ray flux ${\mathcal{F}}_{\mathrm{unfold}}(t)$ is estimated as $\ensuremath{\int}{S}_{\mathrm{unfold}}(E,t)dE$, 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 ${S}_{\mathrm{unfold}}(E,t)$ and ${\mathcal{F}}_{\mathrm{unfold}}(t)$. Noise factors $\ensuremath{\nu}$ are derived from simulations to quantify algorithm-induced changes in the noise-to-signal ratio (NSR) for ${S}_{\mathrm{unfold}}$ in each unfold bin $j$ and for ${\mathcal{F}}_{\mathrm{unfold}}$ ($\ensuremath{\nu}\ensuremath{\equiv}NS{R}_{\mathrm{output}}/NS{R}_{\mathrm{input}}$): for ${S}_{\mathrm{unfold}}$, $1\ensuremath{\lesssim}{\ensuremath{\nu}}_{j}\ensuremath{\lesssim}30$, an outcome that is strongly spectrally dependent; for ${\mathcal{F}}_{\mathrm{unfold}}$, $0.6\ensuremath{\lesssim}{\ensuremath{\nu}}_{\mathcal{F}}\ensuremath{\lesssim}1$, a result that is less spectrally sensitive and corroborated independently. For nominal $z$-pinch experiments, the combined uncertainty (noise and calibrations) in ${\mathcal{F}}_{\mathrm{unfold}}(t)$ at peak is estimated to be $\ensuremath{\sim}15%$. (b) Generalization of the unfold method.---Spectral sensitivities (called here passband functions) are constructed for ${S}_{\mathrm{unfold}}$ and ${\mathcal{F}}_{\mathrm{unfold}}$. 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.
Highlights
A SUMMARY OFPRINCIPAL EQUATIONS FOR THEUNFOLD ALGORITHM IN PART 1Part 1 of this article formulated and tested a spectral unfold algorithm, applied to experimental data DiðtÞ from a filtered-x-ray-detector (XRD) diagnostic of N channels
The diagnostic goal is to estimate the incident x-ray flux F 1⁄2ÁEðtÞ in 1⁄2ÁE from the spectral integral F unfoldðtÞ of SunfoldðE; tÞ. [Equations (1)–(6) below summarize this algorithm.] In part 1 the unfold method was tested in noise-free simulations based on prescribed incident spectra SðE; tÞ: binwise averages, hSij, were compared to the corresponding histogram values SjðtÞ, as were the flux values, F 1⁄2ÁEðtÞ and F unfoldðtÞ
The numerical process, ðf1; . . . ; fNÞ 1⁄4 RÀ1ðD1; . . . ; DNÞ defined in Eq (4), just represents MÀBD1D, which describes the reconstruction for the histogram unfold algorithm
Summary
Part 1 of this article formulated and tested a spectral unfold algorithm, applied to experimental data DiðtÞ from a filtered-x-ray-detector (XRD) diagnostic of N channels (typically, N 1⁄4 5). In this method, the channel-wise response functions RiðEÞ are assumed to be calibrated, and the incident x-ray spectrum SðE; tÞ under diagnosis is presumed to be at least piecewise continuous—though not necessarily Planckian, SbbðE; TÞ. A priori information about the source is used to formulate a reconstruction SunfoldðE; tÞ of SðE; tÞ, where Sunfold is represented by an N-bin histogram over an unfold interval, 1⁄2ÁE 1⁄4 1⁄2ELO; EHI (Pt. 1: Sec. III and Pt. 1: Table II).
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