Statistical Parameter Study of the Time Interval Distribution for Nonparalyzable, Paralyzable, and Hybrid Dead Time Models

Syam, Nur Syamsi; Maeng, Seongjin; Kim, Myo Gwang; Lim, Soo Yeon; Lee, Sang Hoon

doi:10.3938/jkps.72.1133

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Even so, the complete range of the predicted channel intensities {λ i (θ)} was still generated. This was necessary because the P i quantities each depend on a range of the {λ i (θ)} through Equation (5).…”

Section: A1 | Generation Of Synthetic Spectramentioning

confidence: 99%

“…Statistical formulas to correct for the effects of detector dead-time on the recorded intensities are now well-studied, as are dead-time effects in other kinds of instrumentation. [3][4][5][6][7] Early on, Coates provided a practical formula for application to photodetectors 8 and generalized it in a later study. 9 An essentially equivalent result for TOF SIMS data was independently obtained by Stephan et al, who provided correction formulas for both unit-mass and high-resolution spectra.…”

Section: Introductionmentioning

confidence: 99%

“…Statistical formulas to correct for the effects of detector dead‐time on the recorded intensities are now well‐studied, as are dead‐time effects in other kinds of instrumentation 3‐7 . Early on, Coates provided a practical formula for application to photodetectors 8 and generalized it in a later study 9 .…”

Section: Introductionmentioning

confidence: 99%

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High‐resolution peak analysis in TOF SIMS data

Gelb

Esfahani

Walker

2020

Surface & Interface Analysis

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High mass resolution time‐of‐flight secondary ion mass spectrometry (TOF SIMS) can provide a wealth of chemical information about a sample, but the analysis of such data is complicated by detector dead‐time effects that lead to systematic shifts in peak shapes, positions, and intensities. We introduce a new maximum‐likelihood analysis that incorporates the detector behavior in the likelihood function, such that a parametric spectrum model can be fit directly to as‐measured data. In numerical testing, this approach is shown to be the most precise and lowest‐bias option when compared with both weighted and unweighted least‐squares fitting of data corrected for dead‐time effects. Unweighted least‐squares analysis is the next best, while weighted least‐squares suffers from significant bias when the number of pulses used is small. We also provide best‐case estimates of the achievable precision in fitting TOF SIMS peak positions and intensities and investigate the biases introduced by ignoring background intensity and by fitting to just the intense part of a peak. We apply the maximum‐likelihood method to fit two experimental data sets: a positive‐ion spectrum from a multilayer MoS2 sample and a positive‐ion spectrum from a TiZrNi bulk metallic glass sample. The precision of extracted isotope masses and relative abundances obtained is close to the best‐case predictions from the numerical simulations despite the use of inexact peak shape functions and other approximations. Implications for instrument calibration, incorporation of prior information about the sample, and extension of this approach to the analysis of imaging data are also discussed.

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Section: A1 | Generation Of Synthetic Spectramentioning

confidence: 99%