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The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in
order to conduct transformational science. SKAO data products made available to astronomers
will be correspondingly large and complex, requiring the application of advanced analysis
techniques to extract key science findings. To this end, SKAO is conducting a series of Science
Data Challenges, each designed to familiarise the scientific community with SKAO data and
to drive the development of new analysis techniques.
Hartley et al.
present the results from Science
Data Challenge 2 (SDC2), which invited participants to find and characterise 233245 neutral
hydrogen (HI) sources in a simulated data product representing a 2000 h SKA-Mid spectral
line observation from redshifts 0.25 to 0.5. With the support of eight international
supercomputing facilities, including the Australian SKA Regional Centre,
participants were able to undertake the Challenge using dedicated
computational resources. Alongside the main challenge, "reproducibility awards" were made in
recognition of those pipelines which demonstrated Open Science best practice. The Challenge
saw over 100 participants, in 12 teams, develop a range of new and existing techniques, with results that
highlight the strengths of multidisciplinary and collaborative effort. The winning strategy --
which combined predictions from two independent machine learning techniques to yield a
20 percent improvement in overall performance -- underscores one of the main Challenge
outcomes: that of method complementarity. It is likely that the combination of methods in a
so-called ensemble approach will be key to exploiting very large astronomical datasets.
The image above is a 3D view of a sub-set of the full simulated HI emission datacube, containing
2683 HI sources. The cube uses 1286 × 1286 × 6668 pixels to represent
a 1 square degree field of view across the full Challenge frequency range
0.95--1.15 GHz (redshift 0.235--0.495).
The two shorter axes represent the spatial dimensions
and the longer axis the frequency dimension.
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