Automated Detection of Antenna Malfunctions in Large-N Interferometers: A Case Study with the Hydrogen Epoch of Reionization Array

TL;DR

This paper introduces a framework for detecting antenna malfunctions in large radio interferometers, using cross- and auto-correlation metrics, demonstrated on HERA data, to improve data quality and system diagnostics.

Contribution

The paper develops a novel statistical framework and visualization tools for identifying antenna failures, specifically tailored for large interferometric arrays like HERA.

Findings

Effective identification of malfunctioning antennas in HERA data.

Clear visualization methods for systematics detection.

A detailed algorithm for real-data flagging implementation.

Abstract

We present a framework for identifying and flagging malfunctioning antennas in large radio interferometers. We outline two distinct categories of metrics designed to detect outliers along known failure modes of large arrays: cross-correlation metrics, based on all antenna pairs, and auto-correlation metrics, based solely on individual antennas. We define and motivate the statistical framework for all metrics used, and present tailored visualizations that aid us in clearly identifying new and existing systematics. We implement these techniques using data from 105 antennas in the Hydrogen Epoch of Reionization Array (HERA) as a case study. Finally, we provide a detailed algorithm for implementing these metrics as flagging tools on real data sets.