Radio frequency interference mitigation using deep convolutional neural networks

TL;DR

This paper introduces a deep learning approach using U-Net convolutional neural networks to effectively mitigate radio frequency interference in radio telescope data, showing competitive accuracy with traditional methods.

Contribution

The paper presents a novel application of U-Net CNNs for RFI mitigation in radio astronomy, demonstrating its effectiveness and providing an open-source software package.

Findings

U-Net achieves competitive accuracy with classical RFI mitigation algorithms.

The approach is validated on simulated and real telescope data.

The software is publicly available on GitHub.

Abstract

We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE & SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.