Square Kilometre Array Science Data Challenge 3a: foreground removal for an EoR experiment

作者：Anna Bonaldi, Philippa Hartley, Robert D. Braun, S. J. D. Purser, Anshuman Acharya, Kyungjin Ahn, Miguel Aparicio Resco, Omkar Bait, Michele Bianco, A Chakraborty, Emma Chapman, Suman Chatterjee, K Chege, Hao Chen, X Chen, Zhong Chen, Luke Conaboy, M. Cruz, Laura Darriba, M. De Santis, Philipp Denzel, K Diao, Jennifer Feron, Chris Finlay, B. K. Gehlot, S. Ghosh, Sambit K. Giri, R D P Grumitt, Sungwook E. Hong, Takaaki Ito, M Jiang, C. Jordan, Shinna Kim, Minsu Kim, J. Kim, Shreyam Parth Krishna, Akshay Kulkarni, M. López-Caniego, I. Labadie-García, H Lee, D Lee, Nicole Lee, J. Line, Y Liu, Yi Mao, Aishrila Mazumder, Florent Mertens, S. Munshi, Ainulnabilah Nasirudin, S Ni, Viraj Nistane, Carina Norregaard, Donald M. Null, A. R. Offringa, M Oh, Se–Heon Oh, David Parkinson, Jonathan R. Pritchard, M. Ruiz-Granda, V Salvador López, Huanyuan Shan, Rohit Sharma, Cathryn M. Trott, Shintaro Yoshiura, Li Zhang, X Zhang, Q. Zheng, Z Zhu, S Zuo, Takuya Akahori, P. Alberto, Erwan Allys, Tao An, D. Anstey, J Baek, Basavraj, S. A. Brackenhoff, P. Browne, E Ceccotti, Hao Chen, Tianyue Chen, S Choudhuri, M Choudhury, Jonathan Coles, Joseph M. Cook, David Cornu, Steven Cunnington, Sonali Das, E de Lera Acedo Acedo, J.‐M. Delouis, Fēi Dèng, Junjun Ding, Khandakar Md Asif Elahi, P. Fernandez, C Fernández, A Fernández Alcázar, V. Galluzzi, L-Y Gao, Utpal Garain, Julián Garrido, Marie-Lou Gendron-Marsolais, Thomas Gessey-Jones, Hatem Ghorbel, Y Gong, S Guo, Keisuke Hasegawa, Takayuki Hayashi, D. Herranz, V Holanda, A. J. Holloway, Ian Hothi, C. Höfer, Vibor Jelić, Yi Jiang, Lingxiao Jiang, Hao Kang, Jae-Young Kim, L. V. E. Koopmans, R. Lacroix, E Lee, S. A. K. Leeney, F. Levrier, Y Li, Y Liu, Qing-Bo Ma, Romain Mériot, Andrei Mesinger, M. Mevius, Teppei Minoda, M.-A. Miville-Deschênes, J. Moldón, Rajesh Mondal, Chandra Shekhar Murmu, Steven Murray, Nirmala SR, Q Niu, Chuneeta D. Nunhokee, Oscar O’Hara, S K Pal, Sunit Pal, J Park, Manuel Parra-Royón, Narendra Nath Patra, B. Pindor, M. Remazeilles, P Rey, J. A. Rubiño-Martín, S Saha, A. Selvaraj, B. Semelin, R. Shah, Yimin Shao, Abinash Kumar Shaw, F Shi, Haruhiro Shimabukuro, Gaganpreet Singh, Bong Won Sohn, Matteo Stagni, Jean‐Luc Starck, Cong Sui, J. Swinbank, J Sánchez, S. Sánchez–Expósito, Keitaro Takahashi, Tsutomu T. Takeuchi, A Tripathi, L. Verdes‐Montenegro, P. Vielva, Fabio Vitello, G.F. Wang, Qiuliang Wang, X. Wang, Yan Wang, YX Wang, Theresa Wiegert, Alexander Wild, W. L. Williams, Laura Wolz, X Wu, P. Wu, J-Q Xia, Y Xu, Ran Yan, Y-P Yan, Zhongbao Yin, Zhenzhen You, Xiaochun Yu, Keming Yu, Bin Yue, Li Zhang, Ziwei Zhao, X Zhao, Xu Zhou · 发表于：Monthly Notices of the Royal Astronomical Society · 年份：2025 · DOI：10.1093/mnras/staf1466 · 被引用次数：9 · 研究领域：Radio Astronomy Observations and Technology、Astrophysics and Cosmic Phenomena、Synthetic Aperture Radar (SAR) Applications and Techniques

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 assess their 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 $4.2_{-4.2}^{+20} \times 10^{-4}\, \rm {K}^2h^{-3}$cMpc$^{3}$ 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...