Understanding the structure of the Milky Way’s gravitational potential is crucial for predicting the positions and velocities of stars at different points in time. Currently, most researchers model the Milky Way potential using a time-independent parameterized model that makes assumptions about its symmetry and shape. However, this project attempts to evaluate the effectiveness of a different kind of model, which uses basis function expansion, as opposed to an analytical model, to represent the potential. Using a simulation of a Milky Way-like galaxy (where the density is known at all “snapshots” in time), expansion potential models were made for times throughout a period of approximately 3.8 billion years (Gyr).
We integrate orbits for a population of stars in the model over the time interval and compare their physical properties (phase space positions, energy, etc.) to the true values from the present-day to evaluate the accuracy of this potential model relative to the full and computationally expensive simulation, which would be instructive for using similar models for the actual Milky Way. The results suggest that time-dependence is important to consider when modelling a potential over such a long period of time, even if the model potential does not work perfectly for the star particles chosen. They also suggest that an axisymmetric halo potential model is not accurate, at least for the simulated galaxy we observed.
The most important thing I learned from this experience was a better understanding of how many open questions there are in the field of astrophysics. Reading papers about current developments and discoveries helped me realize how dynamic the field is today. On a more tangible note, I learned a great deal about galactic dynamics and how to code in Python, which added to my educational experience with a depth that would be difficult to obtain from a course. Furthermore, it was interesting to see how the topics I learned in the classroom, particularly from my classical mechanics class, turned out being extremely important while researching these topics. It was enjoyable to see these previously abstract ideas being crucial in a tangible setting. Because I liked working on this project so much, I will continue working for Dr. Sanderson this fall as a research assistant.
Understanding the structure of the Milky Way’s gravitational potential is crucial for predicting the positions and velocities of stars at different points in time. Currently, most researchers model the Milky Way potential using a time-independent parameterized model that makes assumptions about its symmetry and shape. However, this project attempts to evaluate the effectiveness of a different kind of model, which uses basis function expansion, as opposed to an analytical model, to represent the potential. Using a simulation of a Milky Way-like galaxy (where the density is known at all “snapshots” in time), expansion potential models were made for times throughout a period of approximately 3.8 billion years (Gyr).
We integrate orbits for a population of stars in the model over the time interval and compare their physical properties (phase space positions, energy, etc.) to the true values from the present-day to evaluate the accuracy of this potential model relative to the full and computationally expensive simulation, which would be instructive for using similar models for the actual Milky Way. The results suggest that time-dependence is important to consider when modelling a potential over such a long period of time, even if the model potential does not work perfectly for the star particles chosen. They also suggest that an axisymmetric halo potential model is not accurate, at least for the simulated galaxy we observed.
The most important thing I learned from this experience was a better understanding of how many open questions there are in the field of astrophysics. Reading papers about current developments and discoveries helped me realize how dynamic the field is today. On a more tangible note, I learned a great deal about galactic dynamics and how to code in Python, which added to my educational experience with a depth that would be difficult to obtain from a course. Furthermore, it was interesting to see how the topics I learned in the classroom, particularly from my classical mechanics class, turned out being extremely important while researching these topics. It was enjoyable to see these previously abstract ideas being crucial in a tangible setting. Because I liked working on this project so much, I will continue working for Dr. Sanderson this fall as a research assistant.