Discovery of multiple Lorentzian components in the X-ray timing properties of the Narrow Line Seyfert 1 Ark 564
I. M. McHardy (1), P. Arevalo (1), P. Uttley (1), I. E. Papadakis (2),, D. P. Summons (1), W. Brinkmann (3), M. J. Page (4) ((1) University of, Southampton, (2) University of Crete, (3) Max-Planck-Institut fuer, extraterrestrische Physik, (4) MSSL)

TL;DR
This study reveals that the X-ray variability of Ark 564 is best described by two Lorentzian components, indicating two distinct regions of variability, and draws parallels with Galactic binary systems in similar accretion states.
Contribution
It introduces a two-Lorentzian model to describe the power spectrum and time lags in Ark 564, extending the analogy between AGN and Galactic X-ray binaries to high accretion rates.
Findings
Power spectrum of Ark 564 shows two clear breaks and Lorentzian components.
Time lags depend on variability time-scale and are explained by the two-Lorentzian model.
Similarity with Galactic binary states in high accretion regimes.
Abstract
We present a power spectral analysis of a 100 ksec XMM-Newton observation of the narrow line Seyfert 1 galaxy Ark~564. When combined with earlier RXTE and ASCA observations, these data produce a power spectrum covering seven decades of frequency which is well described by a power law with two very clear breaks. This shape is unlike the power spectra of almost all other AGN observed so far, which have only one detected break, and resemble Galactic binary systems in a soft state. The power spectrum can also be well described by the sum of two Lorentzian-shaped components, the one at higher frequencies having a hard spectrum, similar to those seen in Galactic binary systems. Previously we have demonstrated that the lag of the hard band variations relative to the soft band in Ark 564 is dependent on variability time-scale, as seen in Galactic binary sources. Here we show that the time-scale…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
