Adam Battle, University of Arizona; Vishnu Reddy, University of Arizona; Roberto Furfaro, University of Arizona; Tanner Campbell, University of Arizona; James Frith, Air Force Research Laboratory; David Monet, Air Force Research Laboratory
Keywords: Spectroscopy, Geostationary, survey, phase angle, Atlas, characterization
Abstract:
We present a summary of the completed RAPTORS I visible spectroscopic atlas of over 60 geostationary satellites visible from Tucson, AZ. The Robotic Automated Pointing Telescope for Optical Reflectance Spectroscopy (RAPTORS) is an automated 0.61-meter, f/4.6 telescope constructed by five engineering students at the University of Arizona. The system is equipped with a Finger Lakes Instrumentation Proline 4710 CCD with 1,056 x 1,027 pixels resulting in a roughly 16 x 16 arcminute field of view. A transmission grating with a spectral resolution of R~30 is installed in a filter wheel in front of the camera, producing a roughly 15 nm resolution in the visible wavelength range (0.45 0.95 microns).
Observations were conducted with a cadence of at least one spectrum per minute, producing an average of ~800 spectra per satellite each night. Data reduction frames include flats once per lunation along with dark- and bias-frames each night. Nightly spectra of a solar analog star for solar reflectance calibrations were collected each night and an additional airmass correction star was also observed most nights to better account for atmospheric extinction effects if needed.
Initial results presented at the AMOS 2021 conference show that visible spectroscopy is a powerful tool for satellite characterization and differentiation. Examples of the 3-dimensional Spectral Phase Map (SPM) showing longitudinal phase angle (LPA) vs. wavelength vs. normalized reflectance (out-of-plane) have the possibility to uniquely fingerprint a target. These SPMs show feature consistency with more traditional, photometric lightcurve methods of GEO characterization while also showing new, flux-neutral color features in the SPM. These flux-neutral features show no obvious feature in the lightcurve, either by maintaining a constant brightness or having a steady-slope change in brightness. In addition to the SPMs, photometry from the zeroth order point source can be extracted from the data to produce panchromatic, uncalibrated lightcurves of the objects.
Initial machine learning applications to the data set show that the SPMs and their corresponding full night median spectra are excellent tools for target characterization and differentiation. Initial analyses show a strong correlation between a targets SPM and its bus type exists for certain bus types. Additionally, machine learning techniques are able to extract potential eigenfeatures from the full set of data that are representative of GEO satellite features. Progress has been made on linking these eigenfeatures to physical features of the satellite to improve the usefulness of the spectral data. Initial results show that the GEO spectral survey meets its goals of providing methods to discriminate targets via lightcurve data and Spectral Phase Maps as well as for fingerprinting individual satellites. It is possible that these spectral data represent the foundations for a GEO satellite taxonomy, which exceeds the expectations of this survey.
In addition to the atlas of all GEO targets visible from Tucson, AZ, a set of targets representative of their bus type were chosen for a more in-depth analysis. This set of data contains regular spectra of three targets in each of four bus types. These twelve targets were observed over the course of a year to measure affects of seasonal variations that alter the lightcurves and SPMs for a given target. This experiment will highlight the importance of full characterization of targets in order to better differentiate objects in the future and extract the meanings of spectral features. This data set will also expand on our capabilities for bus-type identification, spectral modeling for material composition, and improve our understanding of the physical interpretation of eigenfeatures extracted from the data.
Because this visible spectroscopy atlas of the geostationary belt observed from Tucson promises to be such a powerful resource for the Space Domain Awareness community, this paper will focus on detailing the final state of the atlas and making the atlas available to the public. We include details on the methodologies, objects observed, preliminary results, and challenges faced during the project. Future work will focus on results found from machine learning analyses of the data.
Date of Conference: September 27-20, 2022
Track: Non-Resolved Object Characterization