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Correlation-based Beam Calibration of 21cm Intensity Mapping
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Foreground removal presents a significant obstacle in both current and forthcoming intensity mapping surveys. While numerous techniques have been developed that show promise in simulated datasets, their efficacy often diminishes when applied to real-world data. A primary issue is the frequency-dependent variations in the instrumental response. In this paper, we propose a novel approach utilizing the internal cross-correlation among different frequencies to calibrate the beam's frequency fluctuations. Using a simulated dataset that incorporates frequency-dependent random fluctuations into the beam model, we illustrate that our method can achieve considerable improvements over traditional techniques. Our results represent a step forward in enhancing the precision and reliability of foreground removal in intensity mapping surveys.
Forward citations
Cited by 2 Pith papers
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Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation
A two-channel UNet combining frequency differencing and PCA preprocessing recovers the 21-cm HI power spectrum at large scales under realistic beam effects, improving cross-correlation by 5-8% over single-channel baselines.
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Restoring Missing Modes of 21cm Intensity Mapping with Deep Learning: Impact on BAO Reconstruction
A U-Net restores foreground-removed Fourier modes in simulated 21cm intensity maps, preserves BAO reconstruction performance, and transfers from coarse to fine resolutions.
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