Automated Detection of Antenna Malfunctions in Large‐N Interferometers: A Case Study With the Hydrogen Epoch of Reionization Array. Issue 1 (5th January 2022) - Storer, Dara; Dillon, Joshua S.; Jacobs, Daniel C.; Morales, Miguel F.; Hazelton, Bryna J.; Ewall‐Wice, Aaron; Abdurashidova, Zara; Aguirre, James E.; Alexander, Paul; Ali, Zaki S.; Balfour, Yanga; Beardsley, Adam P.; et al.
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. Key Points: Monitoring the performance of a many‐element system is essential to acquiring science‐quality data and requires an automated approach We define metrics based on cross‐correlations that assess the health of the whole array and its component subsystems in an automated way We outline metrics based on autocorrelations that identify systematics and provide a generalizable approach to automated antenna flagging