Poster
2024 workshop program
A Machine Learning Approach to Morphological Distinction of Repeating vs Non-Repeating Fast Radio Bursts.
The work
Abstract
In the realm of radio astronomy, there is a remarkable surge in the detection of fast radio bursts (FRBs). However, there is still a conundrum behind its two distinct subpopulations: repeating and non-repeating. Utilizing the formidable capabilities of the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope, which has already detected over 4000 FRBS (CHIME FRB Catalog-2, soon to be published), our research focuses on whether the two classes of FRBs are fundamentally different. We aim to use this vast dataset of FRBs detected by CHIME and examine the morphological and parametric differences of these FRBs using machine learning algorithms and statistical methodologies.