Awardee: Philip Choi, Associate Professor of Physics and Astronomy
Philip Choi is an observational astronomer and an Associate Professor in the Department of Physics and Astronomy. His research interests include galaxy evolution, star formation, astronomical adaptive optics, and most recently, the detection of potentially hazardous Near-Earth Asteroids.
Title: Incorporating a Community-Based Ecosystem of Astronomy Python Packages (AstroPy) into the Astrophysics Curriculum
Goal: To redesign the Fall 2022 ASTR101: Techniques in Observational Astrophysics course so students are trained in the most current research tool, Astropy
Philip Choi has taught the upper-level course ASTR101: Techniques in Observational Astrophysics on and off for the past fifteen years. As a research methods course, continuous evolution is required to keep pace with hardware, software, and analysis developments in the field. In recent years, the most significant development in the field has been the astronomical community’s migration away from the Image Reduction and Analysis Facility (IRAF) software platform. The course has been slowly moving away from IRAF over the last few years, but to make a clean break and rebuild the course around Python, a decentralized community software development project named AstroPy would be implemented. Migrating course materials can be an arduous process, so over the summer, Professor Choi proposed working closely with a former student and teaching assistant (Pei Qin ‘23) to build Python modules that would replace the suite of IRAF packages on which the course was initially created. The complete integration of AstroPy into the ASTR101 course prepares students to engage immediately in any research setting. The commitment to Python paves the way for introducing Python-based research tools into our lower-level introductory astronomy and physics courses.
Over the past summer, Choi and Pei Qin ‘22 worked tirelessly to migrate all critical tools into AstroPy. Specifically, they started by identifying all aspects of the course that relied on IRAF. Next, they developed a set of Python notebooks that served as both coding tutorials and functional modules. Finally, they debugged the notebooks and integrated them into various aspects of the course, including in-class tutorials, weekly problem sets, and multi-week research labs.
The migration from PyRAF to Astropy has been an enormous success. From a technical perspective, all critical PyRAF tools were successfully ported to Astropy and integrated into the course materials during the summer. In the Fall, students accomplished all of their coursework using Python-based code. Their AstroPy fluency by the end of the semester enabled them to undertake the most ambitious and best-executed set of final research projects I have seen. Four of the five groups developed projects that would have been unfeasible in previous class iterations. Regarding the overall quality, two of the five projects could serve as the basis of actual research papers, and the other three could easily be launched into summer SURPs.
Another way of measuring the project’s success is through Qin’s reflection as a teaching assistant for the project. She noted she was thankful to put her knowledge and prior coding experience in astronomy to use while simultaneously fulfilling her passion for engaging in educational efforts. Indeed, she already plans to draw from this adaptable experience in her mentorship for other classes in physics and computer science, as well as her co-facilitation of the Peer Mentoring in STEM course. Qin reflects, “The intangible lessons I learned are so invaluable and yet so hard to come by through any means other than working so closely with a professor I respect and admire directly on a project like this.”