# Optimizing Sparse RFI Prediction using Deep Learning

## TL;DR

This paper introduces a deep learning approach using a fully convolutional neural network that jointly utilizes amplitude and phase data to improve the identification of radio frequency interference in radio telescope observations, enhancing accuracy and computational efficiency.

## Contribution

The novel use of a deep fully convolutional neural network with combined amplitude and phase data for RFI detection in radio astronomy observations.

## Key findings

- Phase information improves RFI discrimination accuracy.
- The model achieves a recall of 0.81 and F2 score of 0.75 on real HERA data.
- High data throughput of 1.6×10^5 visibilities per hour per GPU.

## Abstract

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.