Are the ultra-high-redshift galaxies at z > 10 surprising in the context of standard galaxy formation models?
Auteurs : L. Y. Aaron Yung, Rachel S. Somerville, Steven L. Finkelstein, Stephen M. Wilkins, Jonathan P. Gardner
Résumé : A substantial number of ultra-high redshift (8 < z < 17) galaxy candidates have been detected with JWST, posing the question: are these observational results surprising in the context of current galaxy formation models? We address this question using the well-established Santa Cruz semi-analytic models, implemented within merger trees from the new suite of cosmological N-body simulations GUREFT, which were carefully designed for ultra-high redshift studies. Using our fiducial models calibrated at z=0, we present predictions for stellar mass functions, rest-frame UV luminosity functions, and various scaling relations. We find that our (dust-free) models predict galaxy number densities at z~11 (z~13) that are an order of magnitude (a factor of ~30) lower than the observational estimates. We estimate the uncertainty in the observed number densities due to cosmic variance, and find that it leads to a fractional error of ~20-30% at z=11 (~30-80% at z=14) for a 100 sq arcmin field. We explore which processes in our models are most likely to be rate-limiting for the formation of luminous galaxies at these early epochs, considering the halo formation rate, gas cooling, star formation, and stellar feedback, and conclude that it is mainly efficient stellar-driven winds. We find that a modest boost of a factor of ~4 to the UV luminosities, which could arise from a top-heavy stellar initial mass function, would bring our current models into agreement with the observations. Adding a stochastic component to the UV luminosity can also reconcile our results with the observations.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.