1995
DOI: 10.1086/176111
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Dead-Time Modifications to Fast Fourier Transform Power Spectra

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Cited by 247 publications
(225 citation statements)
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“…Then, the average PSDs have been Poisson noise-subtracted, and renormalized adopting the squared fractional rms normalization (Miyamoto et al 1991). We corrected the RXTE data for instrumental dead-time (which produces an over-estimate of the intrinsic Poisson noise level) using the dead-time model of Zhang et al (1995Zhang et al ( , 1996. Since the variability power S/N ratio drops at high frequencies, we limited the analysis to frequencies < ∼ 50 Hz.…”
Section: Discussionmentioning
confidence: 99%
“…Then, the average PSDs have been Poisson noise-subtracted, and renormalized adopting the squared fractional rms normalization (Miyamoto et al 1991). We corrected the RXTE data for instrumental dead-time (which produces an over-estimate of the intrinsic Poisson noise level) using the dead-time model of Zhang et al (1995Zhang et al ( , 1996. Since the variability power S/N ratio drops at high frequencies, we limited the analysis to frequencies < ∼ 50 Hz.…”
Section: Discussionmentioning
confidence: 99%
“…The dead-time can also be measured by taking a Fourier transform of time series for a bright source, which would give a broad peak close to the inverse of the dead-time in the power spectrum. This can be compared with that expected for a deadtime corrected Poisson level power (Zhang et al 1995). This yields an estimate of dead-time of around 42 µs (Yadav et al 2016b).…”
Section: The Laxpc Detectorsmentioning
confidence: 93%
“…The rise in the power spectrum at low frequencies (< 60 Hz) is due to low frequency variability of the source while the structure at high frequencies is due to the characteristic effect of dead time. The expected dead time corrected Poisson level power for a system which is non-paralyzable is given by Zhang et al (1995) …”
Section: High Frequency Variabilitymentioning
confidence: 99%