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