Kernel density estimation improves radio source count estimation over binned methods, as shown in simulations and LOFAR data analysis, with a new AstroKDE package.
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A.Rahmati,J.Schaye,A.H.Pawlik,andM.Raičević.Mon
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Simulations indicate SKAO AA4 surveys can trace thermal and nonthermal ISM processes in high-redshift galaxy analogs beyond z=1-3, underscoring nonthermal feedback at cosmic noon.
SEMPER predicts SKA-Mid Band 2 observations in under 20 hours will recover at least 20% of the total SFRD from radio-emitting SFGs up to z≈6, including NIR-dark galaxies.
Simulations of an SKA-Mid survey predict detection of 1.5e4 star-forming galaxies to z~7 with thermal SFR uncertainties of 0.1 dex, enabling constraints on cosmic star formation rate density.
Modeling indicates AME is negligible for distant galaxies at ~10 GHz but may bias single-frequency SFR estimates in resolved nearby galaxies depending on beam size and column density.
Simulated SKA-Mid surveys reach radio-AGN completeness at L_1.4GHz ~ 10^23 W Hz^-1 up to z~6.
This review chapter maps SKA data volume, complexity, and interpretability challenges onto deep learning, generative models, reinforcement learning, and federated learning for source detection, calibration, and discovery.
The paper describes the expected capabilities of the SKA-Mid survey to produce a complete map of the Milky Way's magnetic field in the southern hemisphere using rotation measures and polarized emission.
Overview of how the SKAO will enable studies of star-forming galaxies, AGN co-evolution, and diffuse emission in clusters and the cosmic web using continuum radio observations.
Overview of SKAO radio surveys for galaxy/AGN co-evolution, including tiered surveys, multi-frequency imaging, and synergies with other observatories.
citing papers explorer
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Improving Radio Source Count Estimation Using Kernel Density Estimation
Kernel density estimation improves radio source count estimation over binned methods, as shown in simulations and LOFAR data analysis, with a new AstroKDE package.
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The Role of Artificial Intelligence in the SKA Era
This review chapter maps SKA data volume, complexity, and interpretability challenges onto deep learning, generative models, reinforcement learning, and federated learning for source detection, calibration, and discovery.