X-Ray Reverberation Mapping of Ark 564 Using Gaussian Process Regression
- Creators
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Lewin, Collin
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Kara, Erin
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Wilkins, Dan
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Mastroserio, Guglielmo
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García, Javier A.
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Zhang, Rachel C.
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Alston, William N.
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Connors, Riley
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Dauser, Thomas
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Fabian, Andrew
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Ingram, Adam
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Jiang, Jiachen
- Lohfink, Anne M.
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Lucchini, Matteo
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Reynolds, Christopher S.
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Tombesi, Francesco
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van der Klis, Michiel
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Wang, Jingyi
Abstract
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 active galactic nuclei (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 multitask learning for application in AGN timing. By simultaneously fitting the time lags and the flux spectra with the relativistic reverberation model reltrans, we constrain the mass at 2.3_(-1.3)^(+2.6) x 10^(6)M_(⊙), 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.
Additional Information
E.K., G.M., and J.A.G. acknowledge support from NASA grant 80NSSC20K0575. J.J. acknowledges support from the Leverhulme Trust, the Isaac Newton Trust, and St Edmund's College, University of Cambridge. A.I. acknowledges support from the Royal Society. G.M. and J.A.G. acknowledge support from NASA grant 80NSSC19K1020. J.A.G. also acknowledges support from the Alexander von Humboldt Foundation. This work was partially supported under NASA contract No. NNG08FD60C and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the NuSTAR Operations, Software, and Calibration teams for their support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (nustardas), jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). M.K. acknowledges support through an NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) Spinoza grant. C.S.R. thanks the UK Science and Technology Facilities Council (STFC) for support under the Consolidated Grant ST/S000623/1, as well as the European Research Council (ERC) for support under the European Unionâs Horizon 2020 research and innovation program (grant 834203).Additional details
- Eprint ID
- 118079
- Resolver ID
- CaltechAUTHORS:20221128-494241100.41
- 80NSSC20K0575
- NASA
- 80NSSC19K1020
- NASA
- NNG08FD60C
- NASA
- ST/S000623/1
- Science and Technology Facilities Council (STFC)
- 834203
- European Research Council (ERC)
- Leverhulme Trust
- Isaac Newton Trust
- St. Edmund's College, University of Cambridge
- Royal Society
- Alexander von Humboldt Foundation
- NASA/JPL/Caltech
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)
- Created
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2022-12-20Created from EPrint's datestamp field
- Updated
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2022-12-20Created from EPrint's last_modified field
- Caltech groups
- Space Radiation Laboratory, NuSTAR