Investigation of a Machine learning methodology for the SKA pulsar search pipeline

TL;DR

This paper explores the application of Mask R-CNN, a modern object detection algorithm, for real-time pulsar candidate detection in the SKA pipeline, addressing the challenge of processing petabyte-scale data.

Contribution

It demonstrates the feasibility of using Mask R-CNN for pulsar candidate detection and introduces a custom annotation tool for large dataset labeling.

Findings

Successful detection of candidate signatures on simulation data

Development of an efficient annotation tool

Potential for real-time processing in SKA pipeline

Abstract

The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes such as SKA will be generating petabytes of data in their full scale of operation. Hence experience-based and data-driven algorithms become indispensable for applications such as candidate detection. Here we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom annotation tool was developed to mark the regions of interest in large datasets efficiently. We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset. The paper presents details of this work with a highlight on the future prospects.