Applying convolutional neural networks to classify fast radio bursts detected by the CHIME telescope
department or school: Science, Faculty of
department or school: Physics and Astronomy, Department of
data provider: DSpace
degree campus: UBCV
creator: Yadav, Prateek
issued: 2020-04-21T17:04:33Z
issued: 2020
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/74077
note: Applying Convolutional Neural Networks to Classify FastRadio Bursts Detected by The CHIME TelescopebyPrateek YadavA THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Physics)The University of British Columbia(Vancouver)April 2020c© Prateek Yadav, 2020The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Applying Convolutional Neural Networks to Classify Fast Radio BurstsDetected by The CHIME Telescopesubmitted by Prateek Yadav in partial fulfillment of the requirements for the de-gree of Master of Science in Physics.Examining Committee:Dr. Ingrid H. Stairs, AstronomySupervisorDr. Gary F. Hinshaw, AstronomySupervisory Committee MemberiiAbstractThe Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a novel ra-dio 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 suscep-tible to a large number of false positive triggers from terrestrial sources of RadioFrequency Interference (RFI). This thesis details the description of intensityML, asoftware pipeline designed to generate waterfall plots and automatically classify ra-dio bursts detected by CHIME without explicit RFI-masking and DM-refinement.The pipeline uses a convolutional neural network based classifier trained exclu-sively on the events detected by CHIME, and the classifier has an accuracy, preci-sion and recall of over 99%. It has also successfully discovered several FRBs, bothin real-time and from archival data. The ideas presented in this thesis may play akey role in designing future machine-learning models for FRB classification.iiiLay SummaryFast radio bursts are bright bursts of radio waves that last about a few millisecondsin duration. These radio bursts originate from outside of our galaxy, but their exactorigin…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0389889
language: eng
degree discipline: Physics
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/74077