Nuclear X-ray properties of the peculiar radio-loud hidden AGN 4C+29.30
M. A. Sobolewska (1), Aneta Siemiginowska (1), G. Migliori (1), L., Stawarz (2, 3), M. Jamrozy (3), D. Evans (1), C. C. Cheung (4). ((1), Harvard-Smithsonian Center for Astrophysics, USA. (2) JAXA, Japan. (3), Astronomical Observatory, Jagiellonian University

TL;DR
This study analyzes the X-ray emission of the radio galaxy 4C+29.30 across a broad energy range, revealing complex absorption and reflection features, variability, and characteristics of a hidden, radio-loud AGN with a supermassive black hole.
Contribution
First detailed X-ray spectral analysis of 4C+29.30 revealing its complex absorption, reflection, and variability, suggesting it is a hidden, radio-loud AGN with a thick torus.
Findings
X-ray spectrum shows intrinsic power-law with Gamma ~ 1.56 and cold absorption N_H ~ 5x10^{23} cm^{-2}
Reflection component less absorbed, with N_H < 2.5x10^{22} cm^{-2}
X-ray variability explained by normalization changes rather than absorption variations
Abstract
We present results from a study of a nuclear emission of a nearby radio galaxy, 4C+29.30, over a broad 0.5-200 keV X-ray band. This study used new XMM-Newton (~17 ksec) and Chandra (~300 ksec) data, and archival Swift/BAT data from the 58-month catalog. The hard (>2 keV) X-ray spectrum of 4C+29.30 can be decomposed into an intrinsic hard power-law (Gamma ~ 1.56) modified by a cold absorber with an intrinsic column density N_{H,z} ~ 5x10^{23} cm^{-2}, and its reflection (|Omega/2pi| ~ 0.3) from a neutral matter including a narrow iron Kalpha emission line at the rest frame energy ~6.4 keV. The reflected component is less absorbed than the intrinsic one with an upper limit on the absorbing column of N^{refl}_{H,z} < 2.5x10^{22} cm^{-2}. The X-ray spectrum varied between the XMM-Newton and Chandra observations. We show that a scenario invoking variations of the normalization of the…
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.
