A model for the infrared-radio correlation of main-sequence galaxies at GHz frequencies and its dependence on redshift and stellar mass
Auteurs : J. Schober, M. T. Sargent, R. S. Klessen, D. R. G. Schleicher
Résumé : The infrared-radio correlation (IRRC) of star-forming galaxies can be used to estimate their star formation rate (SFR) based on the radio continuum luminosity at MHz-GHz frequencies. For application in future deep radio surveys, it is crucial to know whether the IRRC persists at high redshift z. Delvecchio et al. (2021) observed that the 1.4 GHz IRRC correlation of star-forming galaxies is nearly z-invariant up to z~4, but depends strongly on the stellar mass M_star. This should be taken into account for SFR calibrations based on radio luminosity. To understand the physical cause of the M_star-dependence of the IRRC and its properties at higher z, we construct a phenomenological model for galactic radio emission involving magnetic fields generated by a small-scale dynamo, a steady-state cosmic ray population, as well as observed scaling relations that reduce the number of free parameters. The best agreement between the model and the characteristics of the IRRC observed by Delvecchio et al. (2021) is found when the efficiency of the SN-driven turbulence is 5% and when saturation of the small-scale dynamo occurs once 0.5% of the kinetic energy is converted into magnetic energy. Generally, we find that the observed mass dependence of the IRRC appears as long as synchrotron emission dominates the galactic radio flux. When extrapolating the reference model to higher redshift, the free-free emission and absorption strongly affect the radio spectrum, which ultimately leads to an inversion of the M_star dependence of the IRRC at z>5. This could be tested with future deep radio observations, which will also probe the dependence of IR/radio flux ratios on galaxy orientation that is predicted by our model for high-z systems.
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.