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김재영

Kim, Jae-Young
Observational Astrophysics Lab
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Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

Author(s)
Anna BonaldiLee, Hye SeungLee, EunyuKim, Jae-Young
Issued Date
2025-09
DOI
10.1093/mnras/staf1466
URI
https://scholarworks.unist.ac.kr/handle/201301/88492
Citation
Monthly Notices of the Royal Astronomical Society, v.542, no.2, pp.1092 - 1119
Abstract
We present and analyse the results of the Science Data Challenge 3a (SDC3a, https://sdc3.skao.int/challenges/foregrounds), an
epoch of reionization (EoR) foreground-removal exercise organized by the Square Kilometre Array Observatory (SKAO) on
SKA simulated data. The challenge ran for 8 months, from 2023 March to October. Participants were provided with realistic
simulations of SKA-Low data between 106 and 196 MHz, including foreground contamination from extragalactic and Galactic emission, instrumental, and systematic effects. They were asked to deliver cylindrical power spectra of the EoR signal, cleaned
from all corruptions, and the corresponding confidence levels. Here, we describe the approaches taken by the 17 teams that
completed the challenge, and we assesstheir performance using different metrics. The challenge results provide a positive outlook
on the capabilities of current foreground-mitigation approaches to recover the faint EoR signal from SKA-Low observations.
The median error committed in the EoR power spectrum recovery is below the true signal for seven teams, although in some
cases, there are some significant outliers. The smallest residual overall is 4.2+20
−4.2 × 10−4 K2h−3cMpc3 across all considered
scales and frequencies. The estimation of confidence levels provided by the teams is overall less accurate, with the true error
being typically underestimated, sometimes very significantly. The most accurate error bars account for 60 ± 20 per cent of the
true errors committed. The challenge results provide a means for all teams to understand and improve their performance. This
challenge indicates that the comparison between independent pipelines could be a powerful tool to assess residual biases and
improve error estimation
Publisher
Oxford University Press
ISSN
0035-8711

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