Zane was born and raised on Maui, where he graduated from Baldwin HS in 2003. After graduation, he joined the U.S. Air Force, where he served for six years as an aerospace maintenance technician. While serving in the military, he earned an associate degree in Aviation Maintenance Technology and the rank of Staff Sergeant (E-5). In 2010, Zane moved back to Maui to continue his education in the Electronics and Computer Engineering Technology (ECET) program at UH Maui College. He hopes obtain a job as an engineer or technician at one of Maui’s high-tech companies. Zane also enjoys surfing, diving, photography, and spending time with his wife and daughter
Home Island: Maui
High School: Baldwin HS
Institute when accepted: UH Maui
Coelostat Characterization to Optimize Tracking
Project Site: UH Institute for Astronomy
Mentor: John Messersmith
Summit time can be costly if your equipment is not operating the way it should. The coelostat at the Institute for Astronomy aids in ensuring equipment is working properly before it is deployed to the summit. The coelostat is a plane mirror that reflects light from a celestial object to a fixed telescope and tracks that object by adjusting to compensate for the rotation of the Earth. To ensure accurate data is retrieved, the coelostat needs to be tracking at its optimum level. The project proposed to measure how well the coelostat tracking and guiding operates in conjunction with the real-time ephemeris software package, TheSky. Using a simple camera, I characterized the coelostat tracking performance by acquiring images and recording how much, on average, these images drifted from a reference image. To determine the best way to optimize the coelostat, we first need to determine how good or how bad the tracking is. This is done by plotting the x and y offsets of the Sun over a period of time while the tracking is on. The offsets are given in units of pixels to measure image shift in the x and y axis. The coelostat guiding program, which is a refinement of the tracking, uses cross-correlation to calculate these offsets. We found a trend in the guiding which revealed that the guiding consistently had a bias of 1 in the x-axis and -1 in the y-axis, contributing to the image drift. To correct for this bias we implemented an over-correction in the guiding code. Our implementation reduced the offsets by 67% thus greatly improving the guiding performance. Future improvements can be to characterize the drift of other celestial objects such as the Moon, planets, and stars leading to a more versatile tracking and guiding mechanism.