PREPRINT
EA462CAE-74AB-45F3-82AB-82DE8A46AF57

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

Shashank Sanjay Bhat, Prabu Thiagaraj, Ben Stappers, Atul Ghalame, Snehanshu Saha, T. S. B Sudarshan, Zaffirah Hosenie
arXiv:2209.04430

Submitted on 9 September 2022

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.

Preprint

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

URL: https://arxiv.org/abs/2209.04430