No time for dead time: Timing analysis of bright black hole binaries with NuSTAR

Matteo Bachetti, Fiona A. Harrison, Rick Cook, John Tomsick, Christian Schmid, Brian W. Grefenstette, Didier Barret, Steven E. Boggs, Finn E. Christensen, William W. Craig, Andrew C. Fabian, Felix Fürst, Poshak Gandhi, Charles J. Hailey, Erin Kara, Thomas J. Maccarone, Jon M. Miller, Katja Pottschmidt, Daniel Stern, Phil UttleyDominic J. Walton, Jörn Wilms, William W. Zhang

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Timing of high-count-rate sources with the NuSTAR Small Explorer Mission requires specialized analysis techniques. NuSTAR was primarily designed for spectroscopic observations of sources with relatively low count rates rather than for timing analysis of bright objects. The instrumental dead time per event is relatively long (∼2.5 msec) and varies event-to-event by a few percent. The most obvious effect is a distortion of the white noise level in the power density spectrum (PDS) that cannot be easily modeled with standard techniques due to the variable nature of the dead time. In this paper, we show that it is possible to exploit the presence of two completely independent focal planes and use the cospectrum, the real part of the cross PDS, to obtain a good proxy of the white-noise-subtracted PDS. Thereafter, one can use a Monte Carlo approach to estimate the remaining effects of dead time, namely, a frequency-dependent modulation of the variance and a frequency-independent drop of the sensitivity to variability. In this way, most of the standard timing analysis can be performed, albeit with a sacrifice in signal-to-noise ratio relative to what would be achieved using more standard techniques. We apply this technique to NuSTAR observations of the black hole binaries GX 339-4, Cyg X-1, and GRS 1915+105.

Original languageEnglish
Article number109
JournalAstrophysical Journal
Issue number2
StatePublished - Feb 20 2015


  • X-rays: stars
  • accretion, accretion disks
  • black hole physics
  • methods: data analysis
  • methods: statistical


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