Automated Detection of Antenna Malfunctions in Large-N Interferometers:
A Case Study with the Hydrogen Epoch of Reionization Array
Abstract
We present a framework for identifying and flagging malfunctioning
antennas in large radio interferometers. Using data from 105 antennas in
the Hydrogen Epoch of Reionization Array (HERA) as a case study, we
outline two distinct categories of metrics designed to detect outliers
along known failure modes of the array: 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. Finally, we provide a
detailed algorithm for implementing these metrics as flagging tools on
real data sets.
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