Spectral Energy Distributions of low-luminosity radio galaxies at z~1-3: a high-z view of the host/AGN connection
Ranieri D. Baldi (1,2), Marco Chiaberge (2,3,4), Alessandro Capetti, (5), Javier Rodriguez-Zaurin (2,6), Susana Deustua (2), William B. Sparks, (2), ((1) SISSA, Trieste, Italy, (2) Space Telescope Science Institute,, Baltimore, USA, (3) INAF-Istituto di Radio Astronomia, Bologna

TL;DR
This study analyzes the spectral energy distributions of 34 high-redshift low-luminosity radio galaxies, revealing their old stellar populations, dust and UV excesses, and diverse properties, providing insights into their host/AGN connection.
Contribution
It introduces a new SED modeling technique (2SPD) that accounts for young stars and dust, improving the understanding of high-z low-luminosity radio galaxy hosts.
Findings
Most hosts are old, massive galaxies similar to local FRIs.
Dust emission and UV excesses suggest ongoing star formation or nuclear activity.
Wide variety of properties, from quasars to old galaxies.
Abstract
We study the Spectral Energy Distributions, SEDs, (from FUV to MIR bands) of the first sizeable sample of 34 low-luminosity radio galaxies at high redshifts, selected in the COSMOS field. To model the SEDs we use two different template-fitting techniques: i) the Hyperz code that only considers single stellar templates and ii) our own developed technique 2SPD that also includes the contribution from a young stellar population and dust emission. The resulting photometric redshifts range from z ~0.7 to 3 and are in substantial agreement with measurements from earlier work, but significantly more accurate. The SED of most objects is consistent with a dominant contribution from an old stellar population with an age ~1 - 3 10^{9} years. The inferred total stellar mass range is ~10^{10} - 10^{12} M(sun). Dust emission is needed to account for the 24micron emission in 15 objects. Estimates 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.
