# Energy and polarization based on-line interference mitigation in radio interferometry

arXiv: https://arxiv.org/abs/2412.14775 · HTML: https://mlsanity.com/paper/2412.14775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Benchmarking and Dissecting the Nvidia Hopper GPU Architecture](https://mlsanity.com/paper/2402.13499/) (2024)
- [Deep reinforcement learning for smart calibration of radio telescopes](https://mlsanity.com/paper/2102.03200/) (2021)
- [Robust Radio Interferometric Calibration Using the t-Distribution](https://mlsanity.com/paper/1307.5040/) (2013)
- [Real-Time RFI Mitigation for the Apertif Radio Transient System](https://mlsanity.com/paper/2001.03389/) (2020)

## Built on by (most cited first, 1 of 1)

- [Polarization based direction of arrival estimation using a radio interferometric array](https://mlsanity.com/paper/2510.15116/) (2025)

## Abstract (arXiv)

Radio frequency interference (RFI) is a persistent contaminant in terrestrial radio astronomy. While new radio interferometers are becoming operational, novel sources of RFI are also emerging. In order to strengthen the mitigation of RFI in modern radio interferometers, we propose an on-line RFI mitigation scheme that can be run in the correlator of such interferometers. We combine statistics based on the energy as well as the polarization alignment of the correlated signal to develop an on-line RFI mitigation scheme that can be applied to a data stream produced by the correlator in real-time, especially targeted at low duty-cycle or transient RFI detection. In order to improve the computational efficiency, we explore the use of both single precision and half precision floating point operations in implementing the RFI mitigation algorithm. This ideally suits its deployment in accelerator computing devices such as graphics processing units (GPUs) as used by the LOFAR correlator. We provide results based on real data to demonstrate the efficacy of the proposed method.