# Deep reinforcement learning for smart calibration of radio telescopes

arXiv: https://arxiv.org/abs/2102.03200 · HTML: https://mlsanity.com/paper/2102.03200/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Distributed Radio Interferometric Calibration](https://mlsanity.com/paper/1502.00858/) (2015)
- [Data Multiplexing in Radio Interferometric Calibration](https://mlsanity.com/paper/1711.10221/) (2017)
- [Statistical Performance of Radio Interferometric Calibration](https://mlsanity.com/paper/1902.10448/) (2019)
- [A parallel & automatically tuned algorithm for multispectral image deconvolution](https://mlsanity.com/paper/1905.08468/) (2019)
- [Stochastic Calibration of Radio Interferometers](https://mlsanity.com/paper/2003.00986/) (2020)

## Built on by (most cited first, 5 of 5)

- [Hint assisted reinforcement learning: an application in radio astronomy](https://mlsanity.com/paper/2301.03933/) (2023)
- [Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning](https://mlsanity.com/paper/2306.03739/) (2023)
- [Energy and polarization based on-line interference mitigation in radio interferometry](https://mlsanity.com/paper/2412.14775/) (2024)
- [Advances on the classification of radio image cubes](https://mlsanity.com/paper/2305.03435/) (2023)
- [Reinforcement Learning for Data-Driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration](https://mlsanity.com/paper/2410.17135/) (2024)

## Abstract (arXiv)

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.