Reproducing the assembly of massive galaxies within the hierarchical cosmogony
Fabio Fontanot (1,2), Pierluigi Monaco (2,3), Laura Silva (3), Andrea, Grazian (4) ((1)Max-Planck-Institute for Astronomy, Heidelberg;, (2)Dipartimento di Astronomia, Universita' di Trieste; (3)INAF-Osservatorio, Astronomico di Trieste; (4)INAF-Osservatorio Astronomico di Roma)

TL;DR
This paper compares galaxy formation model predictions with observations, showing success in reproducing certain datasets but highlighting issues with overabundance of bright and intermediate-mass galaxies, indicating missing feedback processes.
Contribution
It introduces an improved galaxy formation model that successfully matches K- and 850 micron galaxy data using standard assumptions, and analyzes the formation and merging of massive galaxies.
Findings
Successfully reproduces K- and 850 micron galaxy data
Overpredicts bright galaxies at z<1 and intermediate-mass galaxies at z~1
Highlights need for additional feedback mechanisms in models
Abstract
In order to gain insight into the physical mechanisms leading to the formation of stars and their assembly in galaxies, we compare the predictions of the MOdel for the Rise of GAlaxies aNd Active nuclei (MORGANA) to the properties of K- and 850 micron-selected galaxies (such as number counts, redshift distributions and luminosity functions) by combining MORGANA with the spectrophotometric model GRASIL. We find that it is possible to reproduce the K- and 850 micron-band datasets at the same time and with a standard Salpeter IMF, and ascribe this success to our improved modeling of cooling in DM halos. We then predict that massively star-forming discs are common at z~2 and dominate the star-formation rate, but most of them merge with other galaxies within ~100 Myr. Our preferred model produces an overabundance of bright galaxies at z<1; this overabundance might be connected to the…
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.
