New method for subtraction of common background fluctuation for radio camera: Chunked Principal Component Analysis method

TL;DR

This paper introduces ChunkedPCA, a novel algorithm that improves background fluctuation removal in radio camera data, resulting in cleaner signals and better data quality compared to traditional PCA methods.

Contribution

The paper presents ChunkedPCA, an innovative PCA-based algorithm that enhances background subtraction by grouping detector pixels, reducing artifacts, and preserving source flux in radio astronomical data.

Findings

ChunkedPCA reduces artifacts more effectively than conventional PCA.

The method preserves astronomical source flux.

It produces a cleaner baseline in both simulated and real data.

Abstract

We present a new algorithm, ChunkedPCA, to remove common background fluctuations from datasets acquired with a radio camera. ChunkedPCA is an improvement on using PCA to achieve fewer artifacts and better RMS on the cleaned dataset. The proposed algorithm determines the background fluctuation by grouping the detector pixels not used in the direct observation of the source. This group is then used to get the background fluctuation for that time and used to subtract the background from the data of all pixels. We apply ChunkedPCA for the numerical simulation data and a real observation data obtained with the MKID camera on the Nobeyama 45-m telescope to verify the effectiveness of the ChunkedPCA. We confirm that using the ChunkedPCA method preserves the flux of the astronomical sources and produces a cleaner baseline than the conventional PCA method.