AlgoSCR: An algorithm for Solar Contamination Removal from radio interferometric data

TL;DR

This paper introduces AlgoSCR, an eigenvalue-based algorithm designed to effectively remove solar contamination from radio interferometric data, thereby improving the usability of daytime observations for hydrogen intensity mapping.

Contribution

The paper presents a novel eigenvalue analysis algorithm specifically for solar contamination removal in radio interferometry data, applicable to large-scale sky mapping instruments.

Findings

AlgoSCR successfully removes most solar contamination from simulated data.

The algorithm effectively cleans real Tianlai data, enabling better data utilization.

It preserves other sky signals while eliminating solar interference.

Abstract

Hydrogen intensity mapping is a new field in astronomy that promises to make three-dimensional maps of the matter distribution of the Universe using the redshifted $21\,\textrm{cm}$ line of neutral hydrogen gas (HI). Several ongoing and upcoming radio interferometers, such as Tianlai, CHIME, HERA, HIRAX, etc. are using this technique. These instruments are designed to map large swaths of the sky by drift scanning over periods of many months. One of the challenges of the observations is that the daytime data is contaminated by strong radio signals from the Sun. In the case of Tianlai, this results in almost half of the measured data being unusable. We try to address this issue by developing an algorithm for solar contamination removal (AlgoSCR) from the radio data. The algorithm is based on an eigenvalue analysis of the visibility matrix, and hence is applicable only to interferometers. We apply AlgoSCR to simulated visibilities, as well as real daytime data from the Tianlai dish array. The algorithm can remove most of the solar contamination without seriously affecting other sky signals and thus makes the data usable for certain applications.