Expanded Generalized Needlet Internal Linear Combination (eGNILC) Framework for the 21-cm Foreground Removal
Wei-Ming Dai (1), Yin-Zhe Ma (2) ((1) Ningbo University, (2), Stellenbosch University)

TL;DR
This paper introduces an expanded GNILC framework with DCT and RPCA enhancements for improved 21-cm foreground removal, demonstrating effective signal recovery in simulations with minimal power loss.
Contribution
The paper develops the eGNILC method incorporating DCT and RPCA, improving foreground separation and signal recovery in 21-cm intensity mapping.
Findings
eGNILC reduces power loss at low multipoles.
Effective recovery of 21-cm signals in SKA-MID and BINGO simulations.
Minimal power loss (~10-20%) with no instrumental noise.
Abstract
The Generalized Needlet Internal Linear Combination (GNILC) method is a non-parametric component separation algorithm to remove the foreground contamination of the 21-cm intensity mapping data. In this work, we perform the Discrete Cosine Transform (DCT) along the frequency axis in the expanded GNILC framework (denoted eGNILC) which helps reduce the power loss in low multipoles, and further demonstrate its performance. We also calculate the eGNILC bias to modify the criterion for determining the degrees of freedom of the foreground (dof), and embed the Robust Principal Component Analysis (RPCA) in mixing matrix computation to obtain a blind component separation method. We find that the eGNILC bias is related to the averaged domain size and the dof of the foreground but not the underlying 21-cm signal. In case of no beam effect, the eGNILC bias is negligible for simple power law…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
