| dc.description.abstract | Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish H I intensity mapping surv e ys. 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 H I intensity mapping experiments. Ho we ver, assessing whether this approach is viable for future intensity mapping surv e ys, 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 e xpose an y limitations. We rev eal that ev en when aggressiv e fore ground cleaning is required, which causes > 50 per cent ne gativ e bias on the largest scales, the power spectrum can be reconstructed using a transfer function to within sub-per cent 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 autocorrelation power spectrum. We validate a method that utilizes the transfer function variance for error estimation in foreground-cleaned power spectra. Finally, we demonstrate how incorrect fiducial parameter assumptions (up to ±100 per cent 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 per cent bias in reco v ered values). | en_US |
