Reconstruction of Bennu Particle Events From Sparse Data

• Main Authors: John Y. Pelgrift, Erik J. Lessac‐Chenen, Coralie D. Adam, Jason M. Leonard, Derek S. Nelson, Leilah McCarthy, Eric M. Sahr, Andrew Liounis, Michael Moreau, Brent J. Bos, Carl W. Hergenrother, Dante S. Lauretta
• Format: Article
• Language: English
• Published: American Geophysical Union (AGU) 2020-08-01
• Series: Earth and Space Science
• Online Access: https://doi.org/10.1029/2019EA000938

Abstract OSIRIS‐REx began observing particle ejection events shortly after entering orbit around near‐Earth asteroid (101955) Bennu in January 2019. For some of these events, the only observations of the ejected particles come from the first two images taken immediately after the event by OSIRIS‐REx's NavCam 1 imager. Without three or more observations of each particle, traditional orbit determination is not possible. However, by assuming that the particles all ejected at the same time and location for a given event, and approximating that their velocities remained constant after ejection (a reasonable approximation for fast‐moving particles, i.e., with velocities on the order of 10 cm/s or greater, given Bennu's weak gravity), we show that it is possible to estimate the particles' states from only two observations each. We applied this newly developed technique to reconstruct the particle ejection events observed by the OSIRIS‐REx spacecraft during orbit about Bennu. Particles were estimated to have ejected with inertial velocities ranging from 7 cm/s to 3.3 m/s, leading to a variety of trajectory types. Most (>80%) of the analyzed events were estimated to have originated from midlatitude regions and to have occurred after noon (local solar time), between 12:44 and 18:52. Comparison with higher‐fidelity orbit determination solutions for the events with sufficient observations demonstrates the validity of our approach and also sheds light on its biases. Our technique offers the capacity to meaningfully constrain the properties of particle ejection events from limited data.