Benjamin Bercasio was born and raised in O‘ahu, Hawai‘i, and graduated from Pearl City High School in 2022. He is currently pursuing a Bachelor of Science degree in Computer Science with a concentration in Creative Computational Media at the University of Hawai‘i at Manoa. He is considering entering graduate school to do research in rendering technology and virtual/augmented reality. Outside of class, he is an officer at his university’s game development club, helping his peers learn about game development and collaboration in large projects. He is also heavily involved in STEM outreach in Hawai‘i, particularly the state’s Science Olympiad program, where he regularly volunteers as a mentor and event supervisor for astronomy-related events in invitational and state tournaments. He enjoys digital illustration, baking chocolate chip cookies, and playing video games with his friends.
Home Island: O‘ahu
High School: Pearl City High School
Institution when accepted: UH Manoa
Site: Daniel K. Inouye Solar Telescope, Pukalani, Hawai‘i
Mentors: Anthony Santini & David Morris
Project title: Investigating DKIST Enclosure Environmental Effects on Observations using Historical Engineering Data
Project Abstract:
Observations at the Daniel K. Inouye Solar Telescope (DKIST) have to deal with atmospheric turbulence, known as seeing, which reduces image quality and decreases DKIST’s full resolution potential. Seeing occurs from a combination of air movement and temperature gradients, and it can originate in Earth’s atmosphere and the inside of the telescope dome itself. The latter is referred to as dome seeing, which is the focus of this study. Specifically, this project analyzes historical data on temperature, wind, humidity, and enclosure air ventilation status to identify correlations between these parameters and dome seeing, with a focus on internal-external temperature gradients and air ventilation. The data analysis was performed on data collected from September 2023 to June 2025. Data were queried from DKIST’s Engineering Data Store and Ignition systems, which both store data from the observatory’s sensors approximately once per second. Statistical analyses primarily involved mean and quantile calculations, with 95% confidence intervals included to verify the strength of all discussed results. Analysis was done using standard Python data libraries such as pandas, numpy, and matplotlib, along with a custom Python library written using the aforementioned libraries to process and visualize the data. The results will be compiled and reported to DKIST’s Science Operations team to inform future procedural changes and system enhancements to improve seeing conditions in the dome.