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No Time for Dead Time: Use the Fourier Amplitude Differences to Normalize Dead-time-affected Periodograms

Bachetti, Matteo and Huppenkothen, Daniela (2018) No Time for Dead Time: Use the Fourier Amplitude Differences to Normalize Dead-time-affected Periodograms. Astrophysical Journal Letters, 853 (2). Art. No. L21. ISSN 2041-8213. doi:10.3847/2041-8213/aaa83b.

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Dead time affects many of the instruments used in X-ray astronomy, by producing a strong distortion in power density spectra. This can make it difficult to model the aperiodic variability of the source or look for quasi-periodic oscillations. Whereas in some instruments a simple a priori correction for dead-time-affected power spectra is possible, this is not the case for others such as NuSTAR, where the dead time is non-constant and long (~2.5 ms). Bachetti et al. (2015) suggested the cospectrum obtained from light curves of independent detectors within the same instrument as a possible way out, but this solution has always only been a partial one: the measured rms was still affected by dead time because the width of the power distribution of the cospectrum was modulated by dead time in a frequency-dependent way. In this Letter, we suggest a new, powerful method to normalize dead-time-affected cospectra and power density spectra. Our approach uses the difference of the Fourier amplitudes from two independent detectors to characterize and filter out the effect of dead time. This method is crucially important for the accurate modeling of periodograms derived from instruments affected by dead time on board current missions like NuSTAR and Astrosat, but also future missions such as IXPE.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Bachetti, Matteo0000-0002-4576-9337
Huppenkothen, Daniela0000-0002-1169-7486
Additional Information:© 2018 The American Astronomical Society. Received 2017 September 27; revised 2018 January 15; accepted 2018 January 15; published 2018 January 30. We thank the anonymous referee for providing very insightful feedback. We thank David W. Hogg for useful discussions on the topic of Fourier analysis, and Jeff Scargle for useful suggestions and comments. MB is supported in part by the Italian Space Agency through agreement ASI-INAF No. 2017-12-H.0 and ASI-INFN agreement No. 2017-13-H.0. D.H. is supported by the James Arthur Postdoctoral Fellowship and the Moore-Sloan Data Science Environment at New York University. D.H. acknowledges support from the DIRAC Institute in the Department of Astronomy at the University of Washington. The DIRAC Institute is supported through generous gifts from the Charles and Lisa Simonyi Fund for Arts and Sciences, and the Washington Research Foundation. Software: Astropy (Astropy Collaboration et al. 2013), Matplotlib (Hunter 2007), scipy & numpy (van der Walt et al. 2011), stingray (Huppenkothen et al. 2016), HENDRICS (Bachetti 2015, before name change), Jupyter notebooks (Kluyver et al. 2016).
Subject Keywords:methods: data analysis – X-rays: binaries – X-rays: general
Issue or Number:2
Record Number:CaltechAUTHORS:20171002-135222252
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Official Citation:Matteo Bachetti and Daniela Huppenkothen 2018 ApJL 853 L21
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81959
Deposited By: Joy Painter
Deposited On:02 Oct 2017 23:11
Last Modified:15 Nov 2021 19:47

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