Thesis Format
Monograph
UTILIZING MACHINE LEARNING TECHNIQUES FOR DISPERSION MEASURE ESTIMATION IN FAST RADIO BURSTS STUDIES
Degree
Master of Science
Program
Computer Science
Supervisor
Daley, Mark
2nd Supervisor
Houde, Martin
Joint Supervisor
Abstract
Fast Radio Bursts (FRBs) are highly energetic, millisecond-duration bursts of energy of- ten detected from extragalactic sources. Almost two decades after their initial discovery, the sources and underlying physical mechanisms of FRBs remain unknown. As FRB signals propagate through space, they interact with matter, resulting in a temporal dispersion with frequency. The amount of dispersion is quantified through the Dispersion Measure (DM), which specifies the time delay before the arrival of a pulse as a function of frequency. The DM plays a crucial role in estimating the distance to the FRB source and can reveal information about the physical conditions at the source. Furthermore, accurate DM estimations have far-reaching implications beyond the direct study of FRBs. They can significantly impact other areas of astrophysics, such as the study of the intergalactic medium, the mapping of cosmic structures, and providing insights into the fundamental laws of physics. These aspects highlight the importance of precise DM measurements in advancing our understanding of the universe. However, the accurate estimate of the DM is often complicated and subject to human error. Automating and improving DM estimations has become an even more important task with the increasing number of detected FRBs and the commissioning of new FRB experiments. In this thesis, we explore various deep-learning models for estimating FRB DMs. To enhance the results of train- ing models, we create a set of simulated FRB data, as real FRB data are often noisy, to develop extensive training sets. We discuss the outcomes of different deep learning models and identify the most promising ones. This research is crucial for understanding and analyzing future FRB data, which are expected to grow exponentially by the commissioning of new experiments and facilities such as the Square Kilometer Array (SKA).
Summary for Lay Audience
Fast Radio Bursts (FRBs) are brief, intense radiation flashes from distant galaxies, lasting typically milliseconds. Discovered nearly two decades ago, their origins and mechanisms remain unknown. Traveling vast distances, these bursts interact with matter, affecting their arrival times at our telescopes. This interaction is measured by the Dispersion Measure (DM), which helps determine the travel distance and conditions encountered. Measuring the DM is essential not only for studying FRBs but also for insights into the universe’s structure and fundamental physics. Accurately measuring the DM is, however, challenging. This thesis focuses on using advanced machine learning models to improve DM estimations. By training models on simulated data, we aim to develop more accurate methods for analyzing FRB signals. This is crucial for handling the surge in data from upcoming astronomical facilities like the Square Kilometer Array (SKA), poised to enhance our understanding of the universe.
Recommended Citation
Rajabi, Hosein, "UTILIZING MACHINE LEARNING TECHNIQUES FOR DISPERSION MEASURE ESTIMATION IN FAST RADIO BURSTS STUDIES" (2024). Electronic Thesis and Dissertation Repository. 10127.
https://ir.lib.uwo.ca/etd/10127