Kyla is a first year Computer Science student at University of Hawaii at Manoa. She plans to attain her masters degree in computer science, then pursue a career in the cybersecurity field. She is passionate about sharing her love for STEM. Kyla has a passion for STEM and competes in FIRST Tech Challenge (FTC) on Team Magma 9378, FIRST Robotics Competition (FRC) on Team Magma 3008, Oahu Mathematics League, CyberPatriots, GirlsGoCyberStart, and CompuGirls Hawaii. Anywhere she goes in life, she hopes to continue her passion in exciting the world about what she, herself has become so excited in. She looks forward to her future in continuing her journey through the wonders of the cyberworld.
Home Island: Oahu
High School:
Institution when accepted: University of Hawaii at Manoa
Lighting the Way: Enhancing Laser Astronomy Insights through PAM Analysis
Project Site: UH Institute for Astronomy, Hilo, HI
Mentor: James Ou & Christoph Baranec
Project Abstract:
Robo-AO operational information presents an opportunity to assess the potential negative impacts of the growing population of satellites, especially the new mega-constellations, on laser astronomy and the ability to observe the sky. The Robo-AO systems are autonomous laser adaptive-optics instruments designed for high resolution astronomy with few-meter class telescopes. Robo-AO-2 is currently being commissioned at the University of Hawaii 2.2 meter telescope on Mauna Kea. These systems emit a laser into the sky as a reference to understand how to correct for atmospheric disturbance. Predictive Avoidance Messages (PAMs) from US Space Command provide guidance in the form of open/closure windows, and on when and where it is safe to utilize lasers to avoid damaging possible satellites. However, the plain text format of PAMs is difficult to directly comprehend, as it describes information in both spatial and temporal domains. For this project, we are developing a python program that will leverage historical and newly received PAM files to understand the impact of satellite patterns on observations. This visualization and analysis tool will analyze the percentage that each observing region is open, at a given point, how long until the next closure, and to assist with operational decision-making.