Deep reinforcement learning for smart calibration of radio telescopes

TL;DR

This paper introduces reinforcement learning techniques to automate hyperparameter tuning in radio telescope data calibration pipelines, enabling autonomous, adaptable, and efficient calibration across diverse observations.

Contribution

It applies RL algorithms to automate pipeline calibration, a novel approach for handling diverse and large-scale radio telescope data processing tasks.

Findings

RL agents can effectively tune hyperparameters for various observations

The approach reduces human intervention in calibration processes

Autonomous calibration improves data quality and processing efficiency

Abstract

Modern radio telescopes produce unprecedented amounts of data, which are passed through many processing pipelines before the delivery of scientific results. Hyperparameters of these pipelines need to be tuned by hand to produce optimal results. Because many thousands of observations are taken during a lifetime of a telescope and because each observation will have its unique settings, the fine tuning of pipelines is a tedious task. In order to automate this process of hyperparameter selection in data calibration pipelines, we introduce the use of reinforcement learning. We test two reinforcement learning techniques, twin delayed deep deterministic policy gradient (TD3) and soft actor-critic (SAC), to train an autonomous agent to perform this fine tuning. For the sake of generalization, we consider the pipeline to be a black-box system where the summarized state of the performance of the pipeline is used by the autonomous agent. The autonomous agent trained in this manner is able to determine optimal settings for diverse observations and is therefore able to perform 'smart' calibration, minimizing the need for human intervention.