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Lunar Meteoritic Gardening Rate Derived from in Situ LADEE/LDEX Measurements, Geophys

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Lunar Meteoritic Gardening Rate Derived from in Situ LADEE/LDEX Measurements, Geophys Geophysical Research Letters RESEARCH LETTER Lunar meteoritic gardening rate derived from in situ 10.1002/2016GL069148 LADEE/LDEX measurements Key Points: 1 2,3,4 • LDEX directly samples the material Jamey R. Szalay and Mihály Horányi reblanketing the lunar surface 1 2 •Thelunardustdensityhasanaverage Southwest Research Institute, San Antonio, Texas, USA, Laboratory for Atmospheric and Space Physics, University of scale height of approximately 200 km Colorado Boulder, Boulder, Colorado, USA, 3Department of Physics, University of Colorado Boulder, Boulder, Colorado, • 40 μm/Myr of lunar soil is USA, 4Institute for Modeling Plasma, Atmospheres, and Cosmic Dust, Boulder, Colorado, USA redistributed near the equatorial plane by meteoritic bombardment, primarily driven by the apex sporadic source Abstract The Lunar Atmosphere and Dust Environment Explorer (LADEE) orbited the Moon for approximately 6 months, taking data with the Lunar Dust Experiment (LDEX). LDEX was uniquely equipped to characterize the current rate of lunar impact gardening as it measured the very particles Correspondence to: J. R. Szalay, taking part in this process. By deriving an average lunar dust density distribution, we calculate the rate [email protected] at which exospheric dust rains back down onto the lunar surface. Near the equatorial plane, we find that approximately 40 μm/Myr of lunar regolith, with a cumulative size distribution index of 2.7, is redistributed Citation: due to meteoritic bombardment, a process which occurs predominantly on the lunar apex hemisphere. Szalay, J. R., and M. Horányi (2016), Lunar meteoritic gardening rate derived from in situ LADEE/LDEX measurements, Geophys. Res. Lett., 43, doi:10.1002/2016GL069148. 1. Introduction The surfaces of airless bodies are continually bombarded by micrometeoroids, which eject and redistribute Received 13 APR 2016 the surface material in a process called impact gardening [Gault et al., 1974; Arnold, 1975; Morris, 1978]. Impact Accepted 9 MAY 2016 gardening is ubiquitous throughout the solar system and plays a critical role in how surfaces are modified Accepted article online 16 MAY 2016 and evolve over time. Without any appreciable atmosphere, airless bodies are exposed to their ambient plasma environments and undergo space weathering processes [Pieters et al., 2000; Taylor et al., 2001], with an efficacy that depends on the competing impact gardening rate at which the lunar material is resurfaced. Impact gardening plays a role in the evolution of volatiles trapped in permanently shadowed regions, mixing and potentially covering them over time [Hodges, 2002; Hurley et al., 2012]. This process is also expected to be relevant at Mercury [Morgan et al.,1988],wheremeteoroidimpactspeedsandtheirsubsequentimpact ejecta generation can be considerably larger than the Moon [Marchi et al.,2005].Lunarswirls,peculiaralbedo markings on the surface of the Moon, represent an active area of research with multiple competing pro- posed source mechanisms [Kramer et al., 2011; Garrick-Bethell et al., 2011; Glotch et al., 2015; Syal and Schultz, 2015; Poppe et al., 2016]. Any explanation for such albedo markings must be consistent with known impact gardening/resurfacing rates. Scientific instruments or solar panels deployed on the lunar surface are subject to the accumulation of dust from meteoritic bombardment, degrading their performance over time. The Apollo and Lunokhod retrore- flectors placed on the surface of the Moon have degraded considerably. The most likely explanation for this degradation is the accumulation of dust partially covering the reflectors, reducing their ability to reflect [Murphy et al., 2010, 2014]. Data from the Apollo Dust Detector Experiments were used to estimate solar cell degradation as well as buildup of dust on vertically facing solar cells by measuring their output as a func- tion of time [Hollick and O’Brien,2013].Whilefutureinstrumentshavebeenproposedtobetterunderstand these fluxes [Li et al., 2015], currently, quantitative estimates for the buildup of impact ejecta on such surfaces remain relatively unconstrained. ©2016. The Authors. The Lunar Dust Experiment (LDEX) [Horányi and et al., 2014] was an impact ionization dust detector aboard This is an open access article under the the Lunar Atmosphere and Dust Environment Explorer (LADEE) [Elphic et al., 2014]. LDEX recorded average terms of the Creative Commons Attribution-NonCommercial-NoDerivs impact rates of ∼1 hit/min of particles during the lower altitude science phase, with radii of a ≳0.3 μm. Using License, which permits use and the data taken during approximately 6 months of operation, LDEX was able to characterize the dust density distribution in any medium, provided distribution of the lunar dust cloud as a function of time, altitude, and local time (LT). LDEX discovered a the original work is properly cited, the use is non-commercial and no permanently present, asymmetric dust cloud [Horányi et al., 2015]. The grains detected by LDEX return to the modifications or adaptations are made. surface on timescales of a few minutes and are the very same grains which reblanket the lunar surface. SZALAY AND HORÁNYI LUNAR METEORITIC GARDENING RATE 1 Geophysical Research Letters 10.1002/2016GL069148 Figure 1. Lunar dust density for each local time denoted by the colored dots and error bars. The diagram in the top corner shows the local time range for each color. The grey curve with error bars in the bottom panel shows the average over all local times. The dashed line shows the best exponential fit to the average in the bottom panel, replicated in each of the above panels. Each panel covers the same range of arbitrary linear units. The lunar dust density distribution in the equatorial plane was found to be primarily generated by three known sporadic sources: the helion (HE), apex (AP), and antihelion (AH) [Szalay and Horányi, 2015a], with a minor contribution potentially coming from the antiapex (AA) source [Herschel, 1911; Janches et al., 2000; Campbell-Brown, 2008]. The ejecta cloud was observed to be sensitive to small changes in impactor fluxes and velocities. During several of the known meteoroid showers, LDEX observed temporary enhancements of the lunar dust cloud, localized to the hemisphere exposed to the incident meteoroid shower flux [Szalay and Horányi, 2016]. 2. Determining the Average Lunar Dust Density Profile To determine the impact gardening rate, an average vertical structure of the dust density profile must be derived. First, we select data taken during the months of January to April 2014, as this is the time period with minimal meteoroid shower activity [Szalay and Horányi, 2016] and most indicative of the cloud’s average behavior. We calculate the density as a function of altitude for local time (LT) bins of 1.5 h for all available local times. Normalizing each density to the 6 LT profile, we determine an average dust density profile as a function of altitude (Figure 1). For the purposes of this study, an exponential fit is found to be in good agreement, with −h∕" the form n(h)=n0e , where h is the altitude in kilometers above the lunar surface, n0 is the density at h=0, and =200 km is the scale height. The altitude distribution of the dust is then f(h)= 1 e−h∕". " " As a first approximation, f(h) was calculated by averaging all observations over the local times LADEE visited [Horányi et al., 2015, Methods]. Due to LADEE’s orbital characteristics, each sampled local time covers a dif- ferent distribution of altitudes. Here we present an improved method to determine the distribution function by removing the correlation between local time and altitude. Assuming all LDEX detections are for particles at their vertical turning points [Horányi et al.,2015],wecancalculatethevelocityparticleswillreimpactthe surface from any altitude from energy conservation. With the approximation that all grains undergo purely vertical motion, the corresponding one-dimensional velocity distribution function at the surface is R∕" dh 2Rv − 2 f(v)=f (h(v)) = e (ve∕v) −1 , (1) dv 2 2 2 "ve 1 −(v∕ve) ( ) R where ve =2.4 km/s is the lunar escape speed, R=1737 km is the radius of the Moon, and h(v)= 2 from (ve∕v) −1 energy conservation. This distribution function differs from previous work [Horányi et al., 2015, Methods] due to the additional analysis of the local time dependence (Figure 2). SZALAY AND HORÁNYI LUNAR METEORITIC GARDENING RATE 2 Geophysical Research Letters 10.1002/2016GL069148 As mentioned, we restrict our analysis to LDEX data taken during January to April 2014. However, during the commissioning phase of LADEE’s orbit, the spacecraft reached altitudes up to ∼250 km until late November 2013. While the LDEX data dur- ing late 2013 are more variable due to increased meteoroid stream activity, the exponential fit derived here is consistent with the high-altitude data profile observed by LDEX, reinforcing our methodology. The differential mass distribution of lunar ejecta measured by LDEX remained inde- pendent of the altitude, and had the form 1 f(m)∝m−( +#), where # =0.9; hence, the differential size distribution is f a f m Figure 2. The velocity distribution function from equation (1) (black) ( )∝ ( ) dm Ca−(1+3#), with the normalization along with the previously derived distribution (grey) from Horányi da = et al. [2015, Methods]. The vertical dashed line indicates the velocity −3# −3# (C =3)#∕(a0 −amax). LDEX measured parti- to reach the highest altitude visited by LDEX of 250 km. For velocities cles above a threshold size of ath ≈0.3 μm. ≳840 m/s, the distribution function derived in this work is an extrapolation. The average lunar dust density profile in Figure 1 shows the dust density for parti- cles with radii a ≥ ath. Since the measured dust distribution is a simple power law for a > ath [Horányi et al., 2015], we assume the power law to remain valid to a critical value a0≤ ath [Kolmogorov, 1941]. The dust density −3# for particles with radii greater than a is given by n(h, a)=n(h)(a∕ath) where n(h) is fit to the LDEX-derived densities for a≥ath and is assumed to remain valid for all sizes with a≥a0.
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