Emerging Trends In Pulsar Star Studies: A Synthesis Of Machine Learning Techniques In Pulsar Star Research

Abstract

The pulsar is an extremely magnetized gyrating neutron star having a radius of 10 -15 km. Pulsars provide the indirect evidence of the gravitational wave's existence. So, to study the gravitational waves identification of pulsars is mandatory. Pulsars are considered as the Universe's gift. Pulsars provide scientists and researchers with information of the physics of neutron stars, which are thought to be the densest materials in the universe. The reason why astronomers give importance to the pulsars, because they are the leading edge of the research, based on the gravity. All pulsars produce marginally distinct emission pattern and it varies to some extent with every rotation. Hence, a promising signal detection is termed as a candidate, which is averaged based on every rotation of the pulsars. Any absence of the additional information, implies that each candidate is a real pulsar. The valid signals are extremely hard to detect due to noise and radio frequency interference (RFI). To clear up with this issue, Machine Learning (ML) algorithms were used for automatically classifying, identifying and many other process of pulsar candidates. This survey paper talks about different techniques used by different researchers for the pulsar star classification, identification and still more, using ML techniques.