We present data processing and verification of the Southern Twenty-centimetre All-sky Polarization Survey (STAPS) conducted with Murriyang, the Parkes 64-m telescope. The survey covers the sky area of -89<Dec<0 and the frequency range of 1.3-1.8 GHz split into 1-MHz channels. STAPS was observed commensally with the S-band Polarization All-Sky Survey (S-PASS). The survey is composed of long azimuth scans, which allows us to absolutely calibrate Stokes Q and U with the data processing procedure developed for S-PASS. We obtain I, Q, and U maps in both flux density scale (Jy/beam) and main beam brightness temperature scale (K), for the 301 frequency channels with sufficiently good data. The temperature scale is tied to the Global Magneto-ionic Medium Survey (GMIMS) high-band north sky survey conducted with the Dominion Radio Astrophysical Observatory 26-m telescope. All the STAPS maps are smoothed to a common resolution of 20 arcmin. The rms noise per channel ranges from about 16 mK to 8 mK for I, and from about 8 mK to 5 mK for Q and U at frequencies from 1.3 to 1.8 GHz. The rms noise in Q and U varies with declination and reaches minimum at declination of -89 degree. We also run rotation measure (RM) synthesis and RM clean to obtain peak polarized intensity and Faraday depth maps. The whole STAPS data processing is validated by comparing flux densities of compact sources, pixel flux density versus pixel flux density for Cen A, pixel temperature versus pixel temperature for the entire survey area, and RMs of extragalactic sources between STAPS and other measurements. The uncertainty of the flux density scale is less than 10%. STAPS delivers an L-band (20 cm) multi-frequency polarization view of the Galaxy, and will help advance our understanding of the Galactic magnetic field and magnetized interstellar medium.
The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. We investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity and age. We generate noise-free, synthetic high-resolution imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images are generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We use a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a $\leq 0.130 {\rm \,dex}$ scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but still contain significant information which originates from underlying correlations at a sub-kpc scale between stellar mass surface density and stellar population properties.