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Paper   IPM / Astronomy / 16400
School of Astronomy
  Title:   Square Kilometre Array Science Data Challenge 1: analysis and results
1.  A. Bonaldi
2.  T. An
3.  M. Bruggen
4.  S. Burkutean
5.  B. Coelho
6.  H. Goodarzi
7.  P. Hartley
8.  P. K. Sandhu
9.  C. Wu
10.  L. Yu
11.  M. H. Zhoolideh Haghighi
12.  S. Anton
13.  Z. Bagheri
14.  D. Barbosa
15.  J. P. Barraca
16.  D. Bartashevich
17.  M. Bergano
18.  M. Bonato
19.  J. Brand
20.  F. de Gasperin
21.  A. Giannetti
22.  R. Dodson
23.  P. Jain
24.  S. Jaiswal
25.  B. Lao
26.  B. Liu
27.  E. Liuzzo
28.  Y. Lu
29.  V. Lukic
30.  D. Maia
31.  N. Marchili
32.  M. Massardi
33.  P. Mohan
34.  J. B. Morgado
35.  M. Panwar
36.  Prabhakar.
37.  V. A. R. M. Ribeiro
38.  K. L. J. Rygl
39.  V. Sabz Ali
40.  E. Saremi
41.  E. Schisano
42.  S. Sheikhnezami
43.  A. Vafaei Sadr
44.  A. Wong
45.  O. I. Wong
  Status:   Published
  Journal: MNRAS
  Year:  2020
  Supported by:            ipm IPM
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

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