Tracing star formation with non-thermal radio emission

Abstract

A key for understanding the evolution of galaxies and in particular their star formation history will be future ultra-deep radio surveys. While star formation rates (SFRs) are regularly estimated with phenomenological formulas based on the local FIR-radio correlation, we present here a physically motivated model to relate star formation with radio fluxes. Such a relation holds only in frequency ranges where the flux is dominated by synchrotron emission, as this radiation originates from cosmic rays produced in supernova remnants, therefore reflecting recent star formation. At low frequencies synchrotron emission can be absorbed by the freefree mechanism. This suppression becomes stronger with increasing number density of the gas, more precisely of the free electrons. We estimate the critical observing frequency below which radio emission is not tracing the SFR, and use the three well-studied local galaxies M 51, M 82, and Arp 220 as test cases for our model. If the observed galaxy is at high redshift, this critical frequency moves along with other spectral features to lower values in the observing frame. In the absence of systematic evolutionary effects, one would therefore expect that the method can be applied at lower observing frequencies for high redshift observations. However, in case of a strong increase of the typical gas column densities towards high redshift, the increasing free-free absorption may erase the star formation signatures at low frequencies. At high radio frequencies both, free-free emission and the thermal bump, can dominate the spectrum, also limiting the applicability of this method.

Galaxy Evolution in the Radio Band: The Role of Star-forming Galaxies and Active Galactic Nuclei

“…However, on the average synchrotron emission in SFGs is produced on spatial scales much larger than that of the thermal electrons. Moreover, in most of the local galaxy population the physical conditions would imply free-free absorption to become relevant only at very low frequencies ν 100 MHz, although there are some controversial cases related to starburst cores where absorption have been reported to be effective even at ν 1 GHz (e.g., Vega et al 2008;Schober et al 2017). Note also that in high redshift SFGs the expected increase in the average density of the medium is easily offset by the z−dependence induced in the restframe…”

LOFAR/H-ATLAS: the low-frequency radio luminosity–star formation rate relation

“…With new radio interferometer arrays such as the Low Frequency Array (LOFAR; van Haarlem et al 2013), we are able to move toward lower radio frequencies, where the contribution to the radio luminosity from thermal free-free emission becomes increasingly negligible although synchrotron self absorption might become more important (e.g. Israel et al 1992;Kapińska et al 2017;Schober et al 2017). In addition, with the increasing number of surveys at other wavebands, it is possible to use multiwavelength data sets to derive galaxy properties (such as SFR, galaxy mass etc.)…”

The Far-Infrared Radio Correlation at low radio frequency with LOFAR/H-ATLAS

“…Furthermore, lower radio frequencies probe lower-energy electrons, which take longer to radiate away their energy than the more energetic electrons observed at 1.4 GHz, and this results in a relationship between the age of a galaxy's electron population and the radio spectral index (Scheuer & Williams 1968;Blundell & Rawlings 2001;Schober et al 2017). Therefore, even if the FIRC is linear at high frequencies due to some conspiracy, this will not necessarily be the case at low frequencies.…”

“…The detection of variation in the FIRC over those galaxy types, or lack thereof, will provide important information about the models that have been constructed (e.g. Schober et al 2017). Several methods are used to distinguish galaxy types for the purposes of studying the FIRC, particularly to classify these into star-forming galaxies and AGN such as BPT diagrams (Baldwin et al 1981), panchromatic SED-fitting with AGN components (Berta et al 2013;Ciesla et al 2016;Calistro-Rivera et al 2016), and classification based on galaxy colours.…”