Published October 14, 2019 | Version v1
Presentation Open

Radio Surveys Data Analysis in the Visibility Domain

Creators

Description

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).

Files

SKADataChallenge-slidesRivi.pdf

Files (722.1 kB)

Name Size Download all
md5:de567f625e99e5cd4036f44e038de8cc
722.1 kB Preview Download