Radio Surveys Data Analysis in the Visibility Domain

The analysis of radio data has traditionally relied on a set of image reconstruction techniques. However the imaging process introduces artefacts and correlated noise, with subsequent estimates of scientific parameters suffering from systematic errors that are difficult to accurately estimate. Until recently this has not been a major issue, but the increased sensitivities and resolution of the forthcoming generation of radio interferometers, such as SKA will allow new scientific measurements, such as weak lensing, that require more reliable and complete source catalogues, meaning higher accuracy in galaxy detection and characterization. I will talk about new scalable Bayesian methods that may be used for detecting and characterize galaxies directly from visibilities of large-scale radio continuum surveys (Rivi & Miller 2018, Rivi et al 2019, Malyali et al. 2019). In particular I will focus on a new method for radio galaxy detection, which adopts a multimodal nested sampling technique and does not require prior knowledge of the number of galaxies in the field of view. This novel approach is very promising but also computationally very challenging because of the large size of datasets that must be processed and the source number density expected to reach. I will show parallelization strategies of the implemented code for the exploitation of High Performance Computing (HPC) infrastructures and preliminary results obtained both from simulated (SKA-MID phase 1) and real data (JVLA observations of GOODS-N field at 5 GHz)