A model for the infrared-radio correlation of main-sequence galaxies at GHz frequencies and its dependence on redshift and stellar mass

Authors: J. Schober, M. T. Sargent, R. S. Klessen, D. R. G. Schleicher

arXiv: 2210.07919v1 - DOI (astro-ph.GA)
15 pages, 14 figures, submitted to A&A
License: CC BY 4.0

Abstract: 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.

Submitted to arXiv on 14 Oct. 2022

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.