Meteroid Impact Localization Strategy on the Lunar Far Side for ESA's LUMIO Mission

METEOROID IMPACT LOCALIZATION STRATEGY ON THE LUNAR FAR SIDE FOR ESA’S LUMIO MISSION. S. Sughi1, E. Peña-Asensio1, P. Panicucci1, F. Ferrari1, F. Topputo1, C. Giordano1, D. Koschny2, E. Ammannito3, A. Zinzi3, R. Moissl4. 1Department of Aerospace Science and Technology, Politecnico di Milano, Italy ([email protected]), 2Technical University of Munich, Germany, 3Agenzia Spaziale Italiana, 4European Space Agency. Introduction: The Lunar Meteoroid Impact Observer (LUMIO) is a CubeSat mission designed to observe, quantify, and characterize lunar meteoroid impacts [1]. LUMIO is a 12U CubeSat that, after a commissioning phase and a transfer phase, will operate nominally for one year in a quasi-halo orbit around Earth-Moon L2 point [2]. By detecting Lunar Impact Flashes (LIFs)—the brief bursts of light produced when meteoroids strike the Moon’s surface— from the far side of the Moon, LUMIO will extend the coverage of impact monitoring beyond Earthbased telescopes, which are limited to the nearside and affected by weather conditions [3]. However, the LUMIO quasi-halo orbit around L2 in combination with the lunar phases will affect the mapping of the far-side and thus the observable meteoroid impacts. Hence, to properly estimate the meteoroid flux at the Moon, it is necessary to understand which areas of the far side of the Moon will be observed and what their corresponding temporal coverage is. Moreover, once a LIF is detected, it is important to estimate, and with what accuracy, its location on the Moon’s surface. Problem: LUMIO will follow a quasi-halo orbit around L2 for one year of operational phase. Hence, the camera will record the Moon’s surface far side at a variable distance from the Moon while varying the sub-satellite point. Additionally, due to solar illumination, the lunar phases will affect the time period in which LUMIO can actually record LIFs. Additionally, the camera will record LIFs only if the lunar disk is shadowed beyond a set threshold (≈50%, TBD), to avoid excessive frame saturation and to allow the Navigation & Engineering cycle sufficient time to perform their analysis. The combination of these effects will result in a non-uniform coverage of the far-side Moon’s surface and thus non-uniform detection areas for LIFs. To properly estimate the meteoroid flux at the Moon, these non-uniformity effects must be considered. Simulations of the effective observable surface have been conducted to predict the coverage and the losses LUMIO will experience during its one year of operations. Due to the limited amount of data that can be downloaded from LUMIO during flight, it is not possible to obtain a full frame image for each LIF. Instead, a sequence of cropped frame areas around the impact will be stored and sent to Earth. Thus, a Moon feature-fitting method is not possible for localizing the impact on the lunar surface [4, 5]. A possible strategy to localize the LIF involves a kernelbased approach where known SPICE data for LUMIO, Moon, and Sun, combined with camera calibration parameters, allow determination of the impact location on the lunar surface (in the Moon planetocentric reference frame), given the detection time and the triggered pixel. This method can later be complemented with other sources of measurement, which might improve accuracy such as limb fitting, surface feature recognition, star occultation. To understand the suitability of this approach and the related errors of estimation, Monte Carlo simulations have been performed. Methods: The mission’s primary instrument, LUMIO-Cam, is an optical sensor capable of detecting meteoroid impact flashes in both the visible and near-infrared spectral range (450-950 nm). Given the LUMIO, Moon, and Sun kernels, it is possible to create tools to compute and study the LUMIO-Cam footprint on the Moon, with and without considering limitations due to solar illumination. A set of analyses can be performed to assess the LUMIO’s Moon surface coverage during the operational portion of the mission, to better understand what LUMIO-Cam will be able to observe and with what accuracy. Errors in the camera’s calibration (i.e., optical center and focal lengths), LUMIO attitude and position, and LIF image processing have been considered to perform perturbation analyses and thus obtain an estimation of kernel-based LIF localization errors through Monte Carlo simulations (Fig. 3). Thanks to these analyses, it is possible to identify the predominant sources of error to mitigate their influence. Results: With the performed analyses, it is possible to highlight different aspects of what LUMIO will observe, along with its limitations and advantages for the science data processing phase. The results are obtained from two different approaches: deterministic quantity computation using a kernelbased method, and statistical estimation from Monte Carlo simulations. Through the SPICE kernels of LUMIO, the Moon, and the Sun, for example, it is possible to estimate which areas will be covered and during which period, to be later confronted with expected meteoroid stream events (Fig. 1). It can also be calculate the area on the Moon’s surface covered by one pixel during the mission from different portions of the frame (Fig. 2). Results from the Monte Carlo simulations instead, highlight the main critical source of error for LIFs kernel-based localization: the uncertainty in the camera’s optical center position. A not-well calibrated camera can lead to an uncertainty localization area of 20 km x 20 km while, in the worst-case scenario, navigation uncertainties can result in an uncertainty area of only 3 km × 3 km. This show the importance of a proper and repeated in-flight camera calibration to mitigate as possible any source of pointing drift. Conclusions: A set of tools has been developed to analyze what LUMIO-Cam will be able to observe and with what accuracy. SPICE-kernel-based simulations can give a broad description of which areas of the Moon’s surface will be observed and when. This information is crucial to properly scale the number of impacts detected to compute the total meteoroid flux at the Moon. Besides the refinement of the flux density, a confrontation with known meteoroid stream activities could lead to further insights on the near-Earth population. Monte Carlo simulations of the optical performance of the LUMIO-Cam and LUMIO navigation kernels are fundamental for a proper localization of the LIFs on the Moon’s surface. Priorly assess the accuracy of the estimation provides suggestions on which factors should be improved during mission preparation phase C, to mitigate their influence. Acknowledgment: LUMIO is developed under the European Space Agency’s (ESA), and it is led by Politecnico di Milano (PoliMi). It is supported by the Italian Space Agency (ASI), the Norwegian Space Agency (NOSA), United Kingdom Space Agency (UKSA), and Swedish National Space Agency (SNSA). Figure 1 2D Equidistant-Azimuthal colormap (origin in Lat. 0° - Lon. 180°) of LUMIO effective footprint on Moon’s surface normalize in one year of operational mission phase (sum of coverage frequency of an area by LUMIO normalized by operational mission duration). Superimposed scattered plots of LUMIO sub-satellite points during Science (red) and Navigation & Engineering (white) cycles. Figure 2 Example of LUMIO-Cam pixel’s width projected on the Moon’s surface over operative mission phase in a given frame pixel position (central pixel, sub-satellite projection). Figure 3 Example of LUMIO LIF localization and its uncertainty area (blue dots with average value as a red dot) for a fixed time point and pixel location (frame-center) in camera frame. Moon far-side view, centered in Lat. 0° - Lon. 180° (Top), 2D zoom latitude vs. longitude (Bottom). References: [1] Topputo F. et al. (2023) Icarus, 389, 115213. . [2] Cipriano A. M. et al. (2018) Front. Astron. Space Sci., 5, 29. [3] Liakos A. et al. (2024) Astron. Astrophys., 687, A14. [4] Liakos, A., Bonanos, A. Z., Xilouris, E. M., et al. (2020), A&A, 663, A112. [5] Madiedo J.M., Ortiz J. L., Morales N., Cabrera-Cano J.( 2015°), PSS, 111,105.