The foreground transfer function for HI intensity mapping signal reconstruction: MeerKLASS and precision cosmology applications
Steven Cunnington, Laura Wolz, Philip Bull, Isabella P. Carucci, Keith Grainge, Melis O. Irfan, Yichao Li, Alkistis Pourtsidou, Mario G. Santos, Marta Spinelli, Jingying Wang
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25 pages, 20 figures. See Figure 4 for the main demonstration of the transfer function's performance for reconstructing signal loss from foreground cleaning. Accepted for publication in MNRAS
Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish HI intensity mapping surveys. Despite the increasing sophistication of blind techniques, some signal loss will be inevitable across all scales. Constructing a corrective transfer function using mock signal injection into the contaminated data has been a practice relied on for HI intensity mapping experiments. However, assessing whether this approach is viable for future intensity mapping surveys where precision cosmology is the aim, remains unexplored. In this work, using simulations, we validate for the first time the use of a foreground transfer function to reconstruct power spectra of foreground-cleaned low-redshift intensity maps and look to expose any limitations. We reveal that even when aggressive foreground cleaning is required, which causes ${>}\,50\%$ negative bias on the largest scales, the power spectrum can be reconstructed using a transfer function to within sub-percent accuracy. We specifically outline the recipe for constructing an unbiased transfer function, highlighting the pitfalls if one deviates from this recipe, and also correctly identify how a transfer function should be applied in an auto-correlation power spectrum. We validate a method that utilises the transfer function variance for error estimation in foreground-cleaned power spectra. Finally, we demonstrate how incorrect fiducial parameter assumptions (up to ${\pm}100\%$ bias) in the generation of mocks, used in the construction of the transfer function, do not significantly bias signal reconstruction or parameter inference (inducing ${<}\,5\%$ bias in recovered values).
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Revealing cosmological fluctuations in 21cm intensity maps with MeerKLASS: from maps to power spectra
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Key Laboratory of Particle Astrophysics, Institute of High Energy Physics, Chinese Academy of Sciences（高能物理研究所粒子天体物理重点实验室，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Generalised Visibility ILC: a new foreground mitigation strategy for interferometric HI intensity mapping in the low-redshift Universe
Instituto de Física de Cantabria (CSIC-Universidad de Cantabria)（坎塔布里亚物理研究所）；Dpto. de Física Moderna, Universidad de Cantabria（坎塔布里亚大学现代物理系）；Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, The University of Manchester（曼彻斯特大学天体物理学乔德雷尔银行中心）；State Key Laboratory of Radio Astronomy and Technology, Shanghai Astronomical Observatory, CAS（中国科学院上海天文台射电天文与技术国家重点实验室）
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