Semester

Summer

Date of Graduation

2021

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Natalia A. Schmid

Committee Member

Kevin Bandura

Committee Member

Yu Gu

Committee Member

Matthew C. Valenti

Committee Member

Brian D. Woerner

Abstract

Development of new algorithms for the detection of isolated astrophysical pulses is of interest to radio astronomers. Both Fast Radio Bursts (FRBs) and several Rotating Radio Transients (RRATs) were detected through the application of a single pulse search algorithm. The conventional approach to detect astronomical pulses requires an exhaustive search for the correct dispersion measure. Its accelerated versions involve signal processing in Fourier transform space.

In this dissertation, we present several new transform-based approaches for the detection and analysis of astrophysical signals with the latest being the most effective and advanced of all. It is implemented in several steps. First, a spectrogram of a dispersed astrophysical pulse is linearized in observing frequency followed by application of the Radon transform. The result of the transformation is displayed as a two-dimensional function. Next, the function is smoothed using a spatial low-pass filter. Finally, the maximum of the function above 90-degree angle is compared to the maximum of the standard deviation of the noise below 90-degree angle and a decision in favor of an astrophysical pulse present or absent is made. Once pulse is detected, its Dispersion Measure (DM) is estimated by means of a basic equation relating the slope of the linearized dispersed pulse and the DM value. Performance of the algorithm is analyzed by applying it to a set of simulated Fast Radio Bursts, experimental data of Masui pulse and experimental data of seven Rotating Radio Transients. The detection algorithm demonstrates results comparable to and above those by the conventional pulse detection algorithm.

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