# Towards exascale real-time RFI mitigation

## TL;DR

This paper presents a scalable real-time RFI mitigation method based on the offline AOFlagger, implemented on CPUs and GPUs, demonstrating high-quality flagging on various data types and scales, suitable for next-generation radio telescopes.

## Contribution

It introduces a scalable, real-time RFI mitigation approach using AOFlagger algorithms, including a novel history-based flagger, applicable to multiple data formats and scalable to exascale systems.

## Key findings

- Achieves higher quality RFI flagging than simple thresholding.
- Demonstrates scalability on CPUs and GPUs for real-time processing.
- Applicable to various data types and time scales.

## Abstract

We describe the design and implementation of an extremely scalable real-time RFI mitigation method, based on the offline AOFlagger. All algorithms scale linearly in the number of samples. We describe how we implemented the flagger in the LOFAR real-time pipeline, on both CPUs and GPUs. Additionally, we introduce a novel simple history-based flagger that helps reduce the impact of our small window on the data. By examining an observation of a known pulsar, we demonstrate that our flagger can achieve much higher quality than a simple thresholder, even when running in real time, on a distributed system. The flagger works on visibility data, but also on raw voltages, and beam formed data. The algorithms are scale-invariant, and work on microsecond to second time scales. We are currently implementing a prototype for the time domain pipeline of the SKA central signal processor.