Published May 2025 | Published
Journal Article Open

Explaining the UV to X-ray correlation in active galactic nuclei within the framework of X-ray illumination of accretion discs

  • 1. ROR icon Roma Tre University
  • 2. ROR icon Arcetri Astrophysical Observatory
  • 3. ROR icon California Institute of Technology
  • 4. ROR icon University of Crete
  • 5. ROR icon Astronomical Institute
  • 6. ROR icon University of Florence

Abstract

Context. It is established that the ultraviolet (UV) and X-ray emission in active galactic nuclei (AGN) is tightly correlated. This correlation is observed both in low- and high-redshift sources. In particular, observations of large samples of quasars revealed a non-linear correlation between UV and X-rays. The physical origin of this correlation is poorly understood.

Aims. We explore this observed correlation in the framework of the X-ray illumination of the accretion disc by a central source. We showed previously that this model successfully explains the continuum UV/optical time delays, variability, and the broadband spectral energy distribution in AGN.

Methods. We used this model to produce 150 000 model spectral energy distributions, assuming a uniform distribution of the model parameters. We computed the corresponding UV (2500 Å) and X-ray (2 keV) monochromatic luminosities and selected only the model data points that agreed with the observed UV-to-X-ray correlation.

Results. Our results show that the X-ray illuminated accretion disc model can reproduce the observed correlation for a subset of model configurations with a non-uniform distribution of the black hole mass (MBH), accretion rate (/Edd), and power transferred from the accretion disc to the corona (Ltransf/Ldisc). In addition, our results reveal a correlation between MBH and /Edd and between /Edd and Ltransf/Ldisc that explains the observed X-ray-UV correlation. We also present evidence based on observed luminosities that supports our findings. We finally discuss the implications of our results.

Copyright and License

© The Authors 2025.

Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Acknowledgement

We thank the anonymous referee for their constructive feedback. EK and IEP acknowledge support from the European Union’s Horizon 2020 Programme under the AHEAD2020 project (grant agreement n. 871158).

Software References

Software packages used in this study include XSPEC/PyXSPEC (Arnaud 1996), NumPy (Harris et al. 2020), SciPy (Virtanen et al. 2020), Pandas (McKinney 2010), Matplotlib (Hunter 2007), and GetDist (Lewis 2019).

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Additional details

Created:
May 13, 2025
Modified:
May 13, 2025