Investigation of a Machine learning methodology for the SKA pulsar search pipeline
This work addresses the need for efficient data-driven methods in real-time pulsar detection for the SKA telescope, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of detecting pulsar candidates in large-scale radio telescope data by testing Mask R-CNN, a state-of-the-art object detection algorithm, and successfully demonstrated its use on a simulation dataset.
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.