Statistically Stable Estimates of Variance in Radioastronomical Observations as Tools for RFI Mitigation

TL;DR

This paper evaluates robust variance estimation algorithms through simulations and observational data to improve radio astronomy data quality by mitigating strong, sporadic radio frequency interference.

Contribution

It introduces and analyzes statistically stable variance estimation algorithms specifically designed for RFI mitigation in radio astronomy.

Findings

Algorithms effectively reduce RFI impact in observational data

Simulation results confirm robustness of the algorithms

Real observational data demonstrate improved data quality

Abstract

A selection of statistically stable (robust) algorithms for data variance calculating has been made. Their properties have been analyzed via computer simulation. These algorithms would be useful if adopted in radio astronomy observations in the presence of strong sporadic radio frequency interference (RFI). Several observational results have been presented here to demonstrate the effectiveness of these algorithms in RFI mitigation.