Graduation Semester and Year
Fall 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Physics and Applied Physics
Department
Physics
First Advisor
Dr. Amir Shahmoradi
Abstract
Astrophysical studies are limited to the data provided. Data which is inherently prone to a range of biases such as: instrumental, selection, and methodological bias. Quantifying these biases and understanding their origin is critical to ensuring that the results we infer are representative of the physical world.
This is true even when attempting to study high-redshift phenomena, such as Gamma Ray Bursts (GRBs). Due to their immense brightness at high redshifts, GRBs serve as a strong candidate for standard candles in high redshift space. Their use as a standard candle, however, requires a precise understanding of observational biases that affect their detection and characterization.
The goal of this dissertation is to revisit previous astrophysical studies with careful consideration of instrumental and systematic biases. In the first study, we develop a methodology for estimating the redshift of gamma-ray bursts that accounts for both measurement uncertainties and the detection threshold of the Burst and Transient Source Experiment (BATSE). Our findings diverge from earlier works because we avoid relying on phenomenological correlations between intrinsic GRB properties, which are strongly shaped by selection biases that favor the brightest and most energetic events.
In the second study, we re-examine claims regarding the existence of radio-loud and radio-quiet long GRBs (LGRBs). Through a detailed statistical analysis, we demonstrate that the observed differences are unlikely to reflect a physical distinction, but are instead consistent with selection effects and sample incompleteness.
In the third study, we revisit prior work that interpreted a plateau in the duration distribution of LGRBs as evidence for a collapsar origin. We show that plateaus naturally emerge in distributions with strictly positive supports, and can also arise from factors such as sample incompleteness, the convolution of redshift with duration, and the overlapping contributions of short GRBs (SGRBs) within the LGRB population.
Keywords
Gamma-Ray Bursts, Astronomy data analysis, Monte Carlo
Disciplines
Other Astrophysics and Astronomy
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Osborne, Joshua A., "Quantifying and Understanding the Role of Bias in Astrophysical Data" (2025). Physics Dissertations. 186.
https://mavmatrix.uta.edu/physics_dissertations/186