Tanner Campbell, University of Arizona; Vishnu Reddy, University of Arizona; Roberto Furfaro, University of Arizona; Adam Battle, University of Arizona; Peter Birtwhistle, Great Shefford Observatory; Tyler Linder, The Astronomical Research Institute; Scott Tucker, Starizona; Neil Pearson, Planetary Science Institute
Keywords: CIS Lunar, Ground-based Observations, Light Curve Inversion, Bayesian Inversion
Abstract:
Near Earth Object (NEO) 2020 SO, first discovered in September 2020 by the Pan-STARRS1 survey, is believed to be a Centaur rocket booster from the mid 1960’s that has been temporarily recaptured by the Earth out of heliocentric orbit. 2020 SO entered Earth’s Hill sphere in early November 2020, with a close approach in December where it became bright enough (approximately 14 V mag) to be observed by ground-based telescopes. There was one more close approach to the Earth in early February 2021 before the rocket body escaped from the Earth’s gravity back into heliocentric orbit after an encounter with the Moon in March 2021. Here we estimate 2020 SO’s spin state and reflective properties using unfiltered photometric data we collected from multiple telescope sites around the world during the December 2020, and February 2021 close approach. The 95% Highest Posterior Density (HPD) credibility region and Maximum A Posteriori (MAP) spin state and reflective properties of 2020 SO are estimated using Bayes’ theorem via a Markov Chain Monte Carlo (MCMC) sampling of a predictive light-curve simulation based on an anisotropic Phong light reflection model.
The ten estimated parameters are the attitude quaternion (4), angular velocity (3), and the diffusive and specular reflectivity parameters of the body (3) at the start of the observation epoch. This can then be propagated forward in time with the aforementioned light-curve simulation to compare against the telescopic observations. This ten-parameter inversion problem is ill-posed, so the results represent a “best estimate” that is limited by several factors. To improve confidence in results, multiple MCMC sampling tests were performed on each of several diverse data sets. Due to the difficulty of sampling the high rotation period of 2020 SO (The MCMC light-curve inversion is accomplished by specifying some initial distributions for each estimated parameter based on any a priori knowledge. If any a priori knowledge is lacking, then a uniform distribution over an appropriate interval is chosen. By sampling from these parameter distributions many times, and using the predictive model to evaluate goodness of fit with the observed data, a posterior estimate of the true parameter distributions can be formed. This posterior estimate is used to inform on future samplings to bias the sampling towards areas of the parameter space with a higher likelihood. Scaled randomness is added to each iteration of the parameter sampling, so a high number of iterations is required to sufficiently sample the parameter space. The end results are posterior estimates of the likelihood or each chosen parameter conditioned by the observed data and any a priori information.
This serves as a proof of concept with plans to extend this type of light-curve inversion to other objects and include the use of Machine Learning (ML) surrogate model to speed up computation and increase the number of estimated parameters. This method of spin state and reflective property estimation can be applied directly to other near-Earth space objects given observations with a high enough temporal density and knowledge of some approximate physical properties (mainly shape) of the object.
Date of Conference: September 14-17, 2021
Track: Non-Resolved Object Characterization