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Applying convolutional neural networks to classify fast radio bursts detected by the CHIME telescope

University of British Columbia

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a novel radio telescope that is predicted to detect up to several dozens of Fast Radio Bursts (FRBs) per day. However, CHIME’s FRB detection software pipeline is susceptible to a large number of false positive triggers from terrestrial sources of Radio Frequency Interference (RFI). This thesis details the description of intensityML, a software pipeline designed to generate waterfall plots and automatically classify radio bursts detected by CHIME without explicit RFI-masking and DM-refinement. The pipeline uses a convolutional neural network based classifier trained exclusively on the events detected by CHIME, and the classifier has an accuracy, precision and recall of over 99%. It has also successfully discovered several FRBs, both in real-time and from archival data. The ideas presented in this thesis may play a key role in designing future machine-learning models for FRB classification.

Text

2020-04-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74077

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/