Square Kilometre Array Science Data Challenge 1: analysis and results
  As the largest radio telescope in the world, the Square Kilometre Array (SKA)
will lead the next generation of radio astronomy. The feats of engineering
required to construct the telescope array will be matched only by the
techniques developed to exploit the rich scientific value of the data. To drive
forward the development of efficient and accurate analysis methods, we are
designing a series of data challenges that will provide the scientific
community with high-quality datasets for testing and evaluating new techniques.
In this paper we present a description and results from the first such Science
Data Challenge (SDC1). Based on SKA MID continuum simulated observations and
covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8
h, 100 h and 1000 h), SDC1 asked participants to apply source detection,
characterization and classification methods to simulated data. The challenge
opened in November 2018, with nine teams submitting results by the deadline of
April 2019. In this work we analyse the results for 8 of those teams,
showcasing the variety of approaches that can be successfully used to find,
characterise and classify sources in a deep, crowded field. The results also
demonstrate the importance of building domain knowledge and expertise on this
kind of analysis to obtain the best performance. As high-resolution
observations begin revealing the true complexity of the sky, one of the
outstanding challenges emerging from this analysis is the ability to deal with
highly resolved and complex sources as effectively as the unresolved source
population.
