Astroinformatics Challenges from Next-generation Radio Continuum Surveys

TL;DR

Next-generation radio surveys will detect tens of millions of sources, requiring new algorithms and methods for data processing, source extraction, cross-matching, and discovering unexpected phenomena in large datasets.

Contribution

The paper highlights the need for novel algorithms, including machine learning, to handle the data volume and complexity of upcoming radio surveys and enable new astrophysical insights.

Findings

Existing algorithms are inadequate for source extraction.

Large datasets enable new statistical testing of models.

Software development is needed to discover unexpected phenomena.

Abstract

The tens of millions of radio sources to be detected with next-generation surveys pose new challenges, quite apart from the obvious ones of processing speed and data volumes. For example, existing algorithms are inadequate for source extraction or cross-matching radio and optical/IR sources, and a new generation of algorithms are needed using machine learning and other techniques. The large numbers of sources enable new ways of testing astrophysical models, using a variety of "large-n astronomy" techniques such as statistical redshifts. Furthermore, while unexpected discoveries account for some of the most significant discoveries in astronomy, it will be difficult to discover the unexpected in large volumes of data, unless specific software is developed to mine the data for the unexpected.