Physical Characterization of Moon Impactor WE0913A

Abstract

On 2022 March 4, the object known as WE0913A crashed into the Moon after several close flybys of the Earth and the Moon in the previous three months. Leading up to impact, the identity of the lunar impactor was up for debate, with two possibilities: the Falcon 9 from the DSCOVR mission or the Long March 3C from the Chang’e 5-T1 mission. In this paper, we present a trajectory and spectroscopic analysis using ground-based telescope observations to show conclusively that WE0913A is the Long March 3C rocket body (R/B) from the Chang’e 5-T1 mission. Analysis of photometric light curves collected before impact give a spin period of 185.221 ± 6.540 s before the first close Earth flyby on 2022 January 20 and a period of 177.754 ± 0.779 s, both at a 1σ confidence level, before the second close Earth flyby on 2022 February 8. Using Markov Chain Monte Carlo sampling and a predictive light curve simulation based on an anisotropic Phong reflection model, we estimate both physical and dynamical properties of the Chang’e 5-T1 R/B at the start of an observation epoch. The results from the Bayesian analysis imply that there may have been additional mass on the front of the rocket body. Using our predicted impact location, the Lunar Reconnaissance Orbiter was able to image the crater site approximately 7.5 km from the prediction. Comparing the pre- and post-impact images of the location shows two distinct craters that were made, supporting the hypothesis that there was additional mass on the rocket body.

Stingray Sensor System for Persistent Survey of the GEO Belt

“…This periodicity implies that the spacecraft is no longer stabilized but instead is rotating (or tumbling) uncontrolled. Performing a four parameter Fourier fit, as shown by Campbell et al [ 22 ], we can find the period of rotation for all three nights of data. Shown in Figure 13 are the results of the Fourier period analysis.…”

“…Machine learning has been shown to be a very effective tool for satellite characterization [ 4 , 17 , 18 , 19 , 21 , 22 ], but one of the biggest limiting factors in the development of these algorithms for real-world use is the availability of training data of both sufficient quality and volume. Historically, this has been very difficult to accomplish due to the volume of data required for traditional ML algorithms and the comparatively low throughput of traditional telescope systems.…”

“…One of the primary goals of the Stingray system is to address this shortcoming and provide these much-needed data. The data from Stingray also provide an avenue for the validation of physics-based models simulating the translational, rotational, or reflective dynamics of RSOs [ 21 , 22 , 25 ].…”

“…Maneuvers can also be estimated from these data [15,16]. More intricate analyses have been able to recover shape, mass, material properties, conglomerate abstract concepts like behavior, and more [3,[17][18][19][20][21][22]. These data can also serve as a proving ground for new methods of observation association and orbit determination [23,24].…”