Matthew Bold, Lockheed Martin Advanced Technology Center; Greg Madsen, Lockheed Martin Space; Phil Bland, Curtin University; Robert Howie, Curtin University; Ben Hartig, Curtin University; James Mason, Lockheed Martin Advanced Technology Center; Dane McCormack, Lockheed Martin Space; Rod Drury, Lockheed Martin Space
Keywords: Optical SSA, light curves, characterization
Abstract:
There is significant interest in the SSA community in using optical light curves of satellites and debris to infer properties such as tumbling, size, pose, composition, etc. The large number of free parameters make the interpretation of the observations particularly challenging. Comparing data taken at different times from different sensors also introduces an extra level of complexity. Here we describe an analysis of a very large, unique dataset of lightcurves from FireOPAL, a distributed network of sensors built with the same hardware and employing identical image processing systems. These fixed mount sensors measure both the integrated brightness during an exposure and a higher time resolution light curve as an object moves through the field of view. Each unit in the network has recorded data over hundreds of nights, with thousands of lightcurves generated each night. The images are synchronised across the network, providing simultaneous observations of the same objects from different places on Earth. In addition, the use of Bayer filters in the sensors enables simultaneous measurements of RGB colour of the same objects at the same time. We describe our analysis of these data, including light curves of objects of known shape and the pattern of life of objects established over long periods of time.
Date of Conference: September 11-14, 2018
Track: Non-Resolved Object Characterization