OzDES Reverberation Mapping of Active Galactic Nuclei: Final Data Release, Black-Hole Mass Results, & Scaling Relations
H. McDougall, T. M. Davis, Z. Yu, P. Martini, C. Lidman, U. Malik, A. Penton, G. F. Lewis, B. E. Tucker, B. J. S. Pope, S. Allam, F. Andrade-Oliveira, J. Asorey, D. Bacon, S. Bocquet, D. Brooks, A. Carnero Rosell, D. Carollo, A. Carr, J. Carretero, T. Y. Cheng, L. N. da Costa

TL;DR
This paper presents the final data release from OzDES reverberation mapping, providing improved black hole mass estimates, scaling relations, and insights into AGN properties across a broad redshift range.
Contribution
It offers the most comprehensive high-redshift AGN reverberation mapping dataset to date, with refined radius-luminosity relations and black hole mass measurements.
Findings
Constrained black hole masses for 62 AGN using reverberation mapping.
Fitted tight power-law lag-luminosity relations with ~0.25 dex scatter.
Resolved tension in CIV scaling relations by accounting for survey selection effects.
Abstract
Over the last decade, the Australian Dark Energy (OzDES) collaboration has used Reverberation Mapping to measure the masses of high redshift supermassive black holes. Here we present the final review and analysis of this OzDES reverberation mapping campaign. These observations use 6-7 years of photometric and spectroscopic observations of 735 Active Galactic Nuclei (AGN) in the redshift range 0.13-3.85 and bolometric luminosity range 44.3 - 47.5 erg/s. Both photometry and spectra are observed in visible wavelengths, allowing for the physical scale of the AGN broad line region to be estimated from reverberations of the H\b{eta}, MgII and CIV emission lines. We successfully use reverberation mapping to constrain the masses of 62 super-massive black holes, and combine with existing data to fit a power law to the lag-luminosity relation for the H\b{eta} and MgII lines with a scatter of…
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.
