Date of Award

Spring 4-26-2022

Access Type

Thesis - ERAU Login Required

Degree Name

Master of Science in Engineering Physics


Physical Sciences

Committee Chair

Ted von Hippel

First Committee Member

Jason Aufdenberg

Second Committee Member

Sergey V. Drakunov

Third Committee Member

Elliot Robinson


Whenever we see any celestial object, the first thing that comes to our mind is how old that object must be? It is vital to know the universe's age or that of our Galaxy – the Milky Way, to understand how the universe evolved to its current state. First, the Galactic population must be studied to understand the universe. A well-grounded way to understand the Galactic population is to learn about the white dwarfs (WDs) from that particular Galactic population. Understanding the white dwarfs can tell us a lot about the Galaxy's history. White dwarfs are the remnants of stellar evolution, providing an age dating method for various Galactic populations. This thesis focuses on the WDs from the thick disk of the Milky Way galaxy. Statistical methods are used to compute the age of the WDs. These methods are capable of handling complex and large data sets. For this research, the Bayesian hierarchical model is used to estimate the age of the WDs. The Bayesian approach has already proved its credibility in various fields. The Bayesian hierarchical model produces parameter estimators for individual objects. It uses the Bayesian method to estimate the parameters for the posterior distribution in multiple levels. The model can produce robust results from naturally clustered data. Finally, the hierarchical model was implemented on 12 candidate thick disk WDs to estimate their ages which eventually helped to approximately evaluate the mean age of this population within our Milky Way galaxy. The hierarchical model calculated the mean age of 12 thick disk WDs to be 9.6625 ± 0.1378 log10 years. This result demonstrates a proof of concept, but the numerical value of the age should not be considered solemnly due to a few limitations, which will be discussed later in the document. The hierarchical model can be applied to a large data set to get a closer value of the mean age of our Milky Way galaxy.