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GRANDWin: Gain-based Radio-frequency Interference Analysis Using Normalized Deviation with Winsorization for MWA Ultralow-frequency Calibration Data

  • Authors: Wildan Hidayat, Shintaro Yoshiura, Keitaro Takahashi, Cathryn M. Trott, D. Null, C. D. Nunhokee

Wildan Hidayat et al 2026 The Astrophysical Journal 1006 .

  • Provider: AAS Journals

Caption: Figure 9.

This figure presents the 2D auto power-spectrum error before (left) and after (middle) applying GRANDWin, and the ratio of before to after (right). The reduction in uncertainty, particularly at higher frequencies, indicates that winsorization effectively suppresses contamination and reduces the variance of the data.

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