Owen Miller, University of Arizona; Tanner Campbell, University of Arizona; Neil Pearson, University of Arizona; Daniel Gray, Sidereal Technology; Scott Tucker, Starizona; Roberto Furfaro, University of Arizona; Vishnu Reddy, University of Arizona
Keywords: SSA, SDA, Mega-Constellations, Space Traffic Managment, Imaging, Optical Systems
Abstract:
Satellite mega-constellations are large groups of satellites that share a collective mission. The number of these large groups has increased dramatically in the last few years and are expected to continue to grow. Concerns about implications of mega-constellations on ground-based astronomy have guided recent research. A study by Halferty et al. (2022) found that Starlink satellites are bright enough to require ground-based astronomers to create workarounds to avoid contamination of telescopic astronomical data. During their research, three versions of Starlinks were observed. They found that the average magnitude of all observed starlinks was 5.5±0.13 with a standard deviation of 1.12. They found that the average of Darksat was 7.3±0.13 with a standard deviation of 0.7. Finally, they found the average magnitude of Visorsats is 6.0±0.13 with a standard deviation of 0.79.
The current study will build on Halferty et al. (2022) by using Multi-Aperture Optical Array (MOA), a collection of four Schmidt Cassegrain telescopes with cameras and a field of view of 2.4 x 1.8 degrees to simultaneously collect. We use LPR and Sloan g, r, i filters which give simultaneous 4-color photometry to improve our characterization of these target satellites. Our future research goal is to program the system to automatically take observations every clear night. With this we can accrue more data as we wont have to manually operate the telescope every time we want to observe. With a larger sample size we can create a catalog of photometric data on mega-constellations that will improve our knowledge on their impact on ground based astronomy.
Date of Conference: September 19-22, 2023
Track: Satellite Characterization