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.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics; Computer Science - Artificial Intelligence; Computer Science - Machine Learning