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UBC Theses and Dissertations

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

Abstract

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.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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