Deep Learning Assisted Data Inspection for Radio Astronomy

TL;DR

This paper introduces an unsupervised deep learning approach using a Variational Autoencoder to diagnose system health in radio telescopes, enabling visual inspection of failures and improving maintenance efficiency.

Contribution

It presents a novel application of VAE for visual failure diagnosis in radio astronomy, including a prototype web framework for real-time system health monitoring.

Findings

VAE achieves 65-90% accuracy in failure detection.

The model effectively projects high-dimensional data into a low-dimensional space.

Prototype system is being tested at ASTRON observatory.

Abstract

Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relatively frequently. In this work we propose novel use of unsupervised deep learning to diagnose system health for modern radio telescopes. The model is a convolutional Variational Autoencoder (VAE) that enables the projection of the high dimensional time-frequency data to a low-dimensional prescriptive space. Using this projection, telescope operators are able to visually inspect failures thereby maintaining system health. We have trained and evaluated the performance of the VAE quantitatively in controlled experiments on simulated data from HERA. Moreover, we present a qualitative assessment of the the model trained and tested on real LOFAR data. Through the use of a naive SVM classifier on the projected synthesised data, we show that there is a trade-off between the dimensionality of the projection and the number of compounded features in a given spectrogram. The VAE and SVM combination scores between 65% and 90% accuracy depending on the number of features in a given input. Finally, we show the prototype system-health-diagnostic web framework that integrates the evaluated model. The system is currently undergoing testing at the ASTRON observatory.