Estimating the local star formation rate density from ASKAP RACS
Understanding the evolution of the cosmic star formation rate density (SFRD) is key to uncovering how the Universe arrived at its present state. This paper presents a novel and efficient method to estimate the local SFRD, which uses supervised machine learning to first identify a population of star-forming galaxies (SFGs). Next, star-formation rates (SFRs) are determined using the 1.4-GHz radio-continuum emission detected by the Australian Square Kilometre Array Pathfinder (ASKAP). Specifically, a gradient-boosted decision tree model was implemented to classify extragalactic sources from the Beck et al. (2022, MNRAS, 515, 4711) catalogue as either galaxies or quasars using RACS-mid and WISE photometry. The full sample, consisting of 389,392 sources, was partitioned into a 70%-15%-15% split for training, validating, and testing. The optimised model achieved a weighted F1 score of 0.93 and an accuracy of 0.94 on the test dataset, ultimately classifying 336,674 sources as galaxies and 52,718 sources as quasars. Using the resulting $z<0.1$ depth-matched galaxy sample and the photometric redshift predictions from Beck et al. (2022, MNRAS, 515, 4711), a modified 1.4-GHz SFR calibration was determined, yielding a local, completeness-corrected, $z<0.1$ SFRD of $(1.4 \pm 0.5) \times 10^{-2} \; \rm M_{\odot}\, yr^{-1}\,Mpc^{-3}$ using 11,293 sources. This value is consistent with previous results. Thus, this study demonstrates the feasibility of using supervised learning to identify large populations of SFGs in order to investigate the SFRD evolution. This presents an exciting prospect for future, deeper surveys such as EMU, which will enable the cosmic SFRD to be probed out to higher redshifts.