Investigating the influence of radio-faint AGN activity on the infrared-radio correlation of massive galaxies (2509.17536v1)
Abstract: It is well-known that star-forming galaxies (SFGs) exhibit a tight correlation between their radio and infrared emissions, commonly referred to as the infrared-radio correlation (IRRC). Recent empirical studies have reported a dependence of the IRRC on the galaxy stellar mass, in which more massive galaxies tend to show lower infrared-to-radio ratios (qIR) with respect to less massive galaxies. One possible, yet unexplored, explanation is a residual contamination of the radio emission from active galactic nuclei (AGN), not captured through "radio-excess" diagnostics. To investigate this hypothesis, we aim to statistically quantify the contribution of AGN emission to the radio luminosities of SFGs located within the scatter of the IRRC. Our VLBA program "AGN-sCAN" has targeted 500 galaxies that follow the qIR distribution of the IRRC, i.e., with no prior evidence for radio-excess AGN emission based on low-resolution (~ arcsec) VLA radio imaging. Our VLBA 1.4 GHz observations reach a 5-sigma sensitivity limit of 25 microJy/beam, corresponding to a radio brightness temperature of Tb ~ 105 K. This classification serves as a robust AGN diagnostic, regardless of the host galaxy's star formation rate. We detect four VLBA sources in the deepest regions, which are also the faintest VLBI-detected AGN in SFGs to date. The effective AGN detection rate is 9%, when considering a control sample matched in mass and sensitivity, which is in good agreement with the extrapolation of previous radio AGN number counts. Despite the non-negligible AGN flux contamination (~ 30%) in our individual VLBA detections, we find that the peak of the qIR distribution is completely unaffected by this correction. We conclude that residual AGN contamination from non-radio-excess AGN is unlikely to be the primary driver of the M* - dependent IRRC.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.