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Published June 2023 | Published
Journal Article Open

A hard look at the X-ray spectral variability of NGC 7582

Abstract

NGC 7582 (z = 0.005264; D = 22.5 Mpc) is a highly variable, changing-look AGN. In this work, we explore the X-ray properties of this source using XMM–Newton and NuSTAR archival observations in the 3 – 40 keV range, from 2001 to 2016. NGC 7582 exhibits a long-term variability between observations but also a short-term variability in two observations that has not been studied before. To study the variability, we perform a time-resolved spectral analysis using a phenomenological model and a physically motivated model (uxclumpy). The spectral fitting is achieved using a nested sampling Monte Carlo method. uxclumpy enables testing various geometries of the absorber that may fit AGN spectra. We find that the best model is composed of a fully covering clumpy absorber. From this geometry, we estimate the velocity, size, and distance of the clumps. The column density of the absorber in the line of sight varies from Compton-thin to Compton-thick between observations. Variability over the time-scale of a few tens of kiloseconds is also observed within two observations. The obscuring clouds are consistent with being located at a distance not larger than 0.6 pc, moving with a transverse velocity exceeding ∼700 km s⁻¹. We could put only a lower limit on the size of the obscuring cloud being larger than 10¹³ cm. Given the sparsity of the observations, and the limited exposure time per observation available, we cannot determine the exact structure of the obscuring clouds. The results are broadly consistent with comet-like obscuring clouds or spherical clouds with a non-uniform density profile.

Additional Information

© 2023 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. We thank the anonymous referee for their comments and suggestions. ML acknowledges useful discussions with Johannes Buchner on the UXCLUMPY model. DB/ESK/ML acknowledge financial support from the Centre National d'Etudes Spatiales (CNES). ML acknowledges financial support through the UK Science and Technology Facilities Council (STFC). AZ is supported by NASA under award number 80GSFC21M0002. This research has made use of data and/or software provided by the heasarc, which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. This research made use of Astropy, a community-developed core python package for Astronomy (Astropy Collaboration et al. 2013, 2018), numpy (Harris et al. 2020), and matplotlib, a python library for publication quality graphics (Hunter 2007). This work has made use of GetDist (Lewis 2019), a python library for analysing Monte Carlo samples. This research made use of xspec (Arnaud 1996). We have 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. NuSTAR data was reduced with the NuSTAR Data Analysis Software (NuSTARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). We have also made use of data based on observations obtained with XMM–Newton, an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA. Data Availability: The data used in this paper are publicly available to access and download from the High Energy Astrophysics Science Archive Research Center (heasarc) for NuSTAR and from the XMM–Newton Science Archive. Final data products from this study can be provided on reasonable request to the corresponding author.

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Additional details

Created:
August 22, 2023
Modified:
October 20, 2023