Optimizing Sparse RFI Prediction using Deep Learning
  Radio Frequency Interference (RFI) is an ever-present limiting factor among
radio telescopes even in the most remote observing locations. When looking to
retain the maximum amount of sensitivity and reduce contamination for Epoch of
Reionization studies, the identification and removal of RFI is especially
important. In addition to improved RFI identification, we must also take into
account computational efficiency of the RFI-Identification algorithm as radio
interferometer arrays such as the Hydrogen Epoch of Reionization Array grow
larger in number of receivers. To address this, we present a Deep Fully
Convolutional Neural Network (DFCN) that is comprehensive in its use of
interferometric data, where both amplitude and phase information are used
jointly for identifying RFI. We train the network using simulated HERA
visibilities containing mock RFI, yielding a known "ground truth" dataset for
evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model
is performed on observations from the 67 dish build-out, HERA-67, and achieves
a data throughput of 1.6$\times 10^{5}$ HERA time-ordered 1024 channeled
visibilities per hour per GPU. We determine that relative to an amplitude only
network including visibility phase adds important adjacent time-frequency
context which increases discrimination between RFI and Non-RFI. The inclusion
of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and
$F_{2}$ score of 0.75 as applied to our HERA-67 observations.
