Award Date


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Physics and Astronomy

First Committee Member

Bing Zhang

Second Committee Member

Stephen Lepp

Third Committee Member

Zhaohuan Zhu

Fourth Committee Member

Amei Amei


The discovery of cosmic neutrino flux by IceCube, and the multi-messenger observations of gravitational event GW170817 ushered in the era of multi-messenger astronomy. Since the Universe itself is a natural laboratory, multi-messenger astronomy can help us study the most extreme physics processes in great detail. In this dissertation, we touch on some of the currently unanswered questions involving different types of transient sources and different “messengers” of multi-messenger astronomy. We employ a variety of analysis methods, including machine learning, a method that has not yet been widely adopted in astronomy but is rapidly gaining momentum.We start this dissertation with Chapter 1 and a brief introduction to transient sources and multi-messenger astronomy, as well as machine learning. The subsequent chapters are organized as follows: In Chapter 2, we use publicly released IceCube neutrino data and blazar locations to test the spatial correlation between neutrino events and blazars. We also scrutinize the correlation between γ-ray flux and neutrino flux of blazars, and we find no compelling evidence to prove blazars as the main source of cosmic neutrinos. In Chapter 3, We utilize IceCube neutrino and CHIME fast radio burst (FRB) catalogs to examine the possibility of an association between neutrinos and FRBs. Our results rule out the above-mentioned association. We find the upper limit of the contribution of FRBs to the diffuse cosmic neutrino flux at 100TeV to be ∼7.95×10−21GeV−1cm−2s−1sr−1, or ∼0.55% of the 10-year diffuse neutrino flux observed by IceCube. In Chapter 4, we conduct a global test of delay and jet models of binary neutron star mergers with short gamma-ray bursts (SRGBs) simulated with a Las Vegas algorithm. Our simulations suggest that all SGRBs can be understood with a universal structured jet viewed at different angles. Furthermore, models invoking a jet plus cocoon structure with a lognormal delay timescale are most favored, while the Gaussian delay with the Gaussian jet model and the entire set of power-law delay models are disfavored. In Chapter 5, we train machine learning algorithms with FRBs in the first CHIME/FRB catalog, telling them the repetitiveness of each FRB. We find that the models can predict most FRBs correctly, hinting toward distinct mechanisms behind repeating and non-repeating FRBs. The two most important distinguishing factors between non-repeating and repeating FRBs are brightness temperature and rest-frame frequency bandwidth. We also identify some potentially repeating FRBs currently reported as non-repeating. In Chapter 6, we seek to build a GRB multi-parameter classification scheme with supervised machine learning methods. We utilize the GRB Big Table and Greiner’s GRB catalog, and we divide the input feature into three subgroups: prompt emission, afterglow, and host galaxy. We find that the prompt emission subgroup performs the best. We also find the most important distinguishing feature in prompt emission to be T90, hardness ratio, and fluence. After building the machine learning model, we apply it to the classification of currently unclassified GRBs.


Blazars; Fast radio bursts; Gamma-ray bursts; Machine learning; Neutrino


Astrophysics and Astronomy

File Format


File Size

4200 KB

Degree Grantor

University of Nevada, Las Vegas




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