X-ray Reverberation Mapping of Ark 564 using Gaussian Process Regression
| Autor: | Collin Lewin, Erin Kara, Dan Wilkins, Guglielmo Mastroserio, Javier A. García, Rachel C. Zhang, William N. Alston, Riley Connors, Thomas Dauser, Andrew Fabian, Adam Ingram, Jiachen Jiang, Anne Lohfink, Matteo Lucchini, Christopher S. Reynolds, Francesco Tombesi, Michiel van der Klis, Jingyi Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | The Astrophysical Journal |
| Popis: | Ark 564 is an extreme high-Eddington Narrow-line Seyfert 1 galaxy, known for being one of the brightest, most rapidly variable soft X-ray AGN, and for having one of the lowest temperature coronae. Here we present a 410-ks NuSTAR observation and two 115-ks XMM-Newton observations of this unique source, which reveal a very strong, relativistically broadened iron line. We compute the Fourier-resolved time lags by first using Gaussian processes to interpolate the NuSTAR gaps, implementing the first employment of multi-task learning for application in AGN timing. By fitting simultaneously the time lags and the flux spectra with the relativistic reverberation model RELTRANS, we constrain the mass at $2.3^{+2.6}_{-1.3} \times 10^6M_\odot$, although additional components are required to describe the prominent soft excess in this source. These results motivate future combinations of machine learning, Fourier-resolved timing, and the development of reverberation models. 19 pages, 9 figures. Accepted for publication in The Astrophysical Journal |
| Databáze: | OpenAIRE |
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