A hybrid model for the evolution of galaxies and Active Galactic Nuclei in the infrared
Zhen-Yi Cai (1, 2), Andrea Lapi (3, 1), Jun-Qing Xia (4, 1),, Gianfranco De Zotti (1, 5), Mattia Negrello (5), Carlotta Gruppioni (6),, Emma Rigby (7), Guillaume Castex (8), Jacques Delabrouille (8), Luigi Danese, (1) ((1) Astrophysics Sector, SISSA, (2) Department of Astronomy

TL;DR
This paper introduces a hybrid physical and phenomenological model to explain the evolution of infrared luminosity functions of galaxies and AGNs across different redshifts, accounting for observed counts and background contributions.
Contribution
It presents a novel combined physical and parametric approach to model IR galaxy and AGN evolution, successfully fitting multi-wavelength data and explaining complex observational phenomena.
Findings
Model reproduces observed IR luminosity functions across redshifts.
Accurately predicts galaxy and AGN counts and contributions to the cosmic infrared background.
Explains the transition from positive to negative evolution at z~2.5.
Abstract
[Abridged] We present a comprehensive investigation of the cosmological evolution of the luminosity function (LF) of galaxies and active galactic nuclei (AGN) in the infrared (IR). Based on the observed dichotomy in the ages of stellar populations of early-type galaxies on one side and late-type galaxies on the other, the model interprets the epoch-dependent LFs at z \geq 1.5 using a physical model for the evolution of proto-spheroidal galaxies and of the associated AGNs, while IR galaxies at z<1.5 are interpreted as being mostly late-type 'cold' (normal) and 'warm' (starburst) galaxies. As for proto-spheroids, in addition to the epoch-dependent LFs of stellar and AGN components separately, we have worked out the evolving LFs of these objects as a whole (stellar plus AGN component). The model provides a physical explanation for the observed positive evolution of both galaxies and AGNs…
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.
