Investigation of a Machine learning methodology for the SKA pulsar search 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 are being investigated 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 semi-auto annotation tool was developed and investigated to rapidly mark the regions of interest in large datasets. We have used a simulation dataset to train and build the candidate detection algorithm. A more detailed analysis is planned. This paper presents details of this initial investigation highlighting the future prospects.