Rodel Tagalicud was born and raised on the Big Island of Hawaii. He has recently graduated from the University of Hawaii at Hilo with a BS in Computer Science and BA in Mathematics. While Rodel has relevant experience with data science, machine learning, and AI, he wants to explore the various different fields of Computer Science to hopefully find what suits him best. His primary goal right after graduating is to continually grow and expand his skillset to stay relevant and competitive in this rapidly evolving industry. Rodel enjoys playing video games, music, and sports.
Home Island: Big Island Hawai’i
High School:
Institution when accepted: University of Hawai’i at Hilo
Project Site: Institute for Astronomy, Hilo: IfA, Hilo, Big Island HI
Mentor: Luke McKay
Project Title: Developing a New Detection Algorithm for Optical Telescope Guider Systems
Abstract:
The University of Hawai?i’s 2.2-meter (UH 2.2-m) telescope, located near the summit of Maunakea on Hawai’i Island, plays a critical role in asteroid tracking and other astronomical observations. The telescope is equipped with a wide-field imager off-axis guide system that utilizes a CMOS camera to capture the guide star images. The guider system corrects for small telescope motions caused by variables like temperature differences, alignment errors, and atmospheric conditions, which can cause the telescope to deviate from its target. The current guiding algorithm, based on SEP (Source Extractor for Python), has limitations including slow performance, instability under varying background conditions, insensitivity to low brightness stars, and difficulty with tracking fast-moving celestial objects like asteroids and comets. This project evaluated various methods for improving the guiding system. Some of these methods included processing techniques, such as edge detection and image registration. A pre-trained machine learning model, specifically YOLOv8, was chosen for its simplicity and effectiveness in creating a proof-of-concept. YOLOv8 demonstrated robust real-time object detection capabilities, making it the best option based on criteria such as simplicity, feasibility, and potential for significant improvements in guiding accuracy and efficiency. By integrating these modern techniques, we aim to enhance the UH 2.2-m telescope’s guiding system, optimizing its performance for future astronomical observations.