The Dingle Dell meteorite: a Halloween treat from the Main Belt
Hadrien A. R. Devillepoix, Eleanor K. Sansom, Philip A. Bland, Martin C. Towner, Martin Cupák, Robert M. Howie, Trent Jansen-Sturgeon, Morgan A. Cox, Benjamin A. D. Hartig, Gretchen K. Benedix, Jonathan P. Paxman
DDraft version May 1, 2018
Typeset using L A TEX default style in AASTeX61
THE DINGLE DELL METEORITE: A HALLOWEEN TREAT FROM THE MAIN BELT
Hadrien A. R. Devillepoix, Eleanor K. Sansom, Philip A. Bland, Martin C. Towner, Martin Cup´ak, Robert M. Howie, Trent Jansen-Sturgeon, Morgan A. Cox, Benjamin A. D. Hartig, Gretchen K. Benedix, and Jonathan P. Paxman School of Earth and Planetary Sciences, Curtin University, Bentley, WA 6102, Australia School of Civil and Mechanical Engineering, Curtin University, Bentley, WA 6102, Australia
ABSTRACTWe describe the fall of the Dingle Dell (L/LL 5) meteorite near Morawa in Western Australia on October 31, 2016.The fireball was observed by six observatories of the Desert Fireball Network (DFN), a continental scale facilityoptimised to recover meteorites and calculate their pre-entry orbits. The 30 cm meteoroid entered at 15.44 km s − ,followed a moderately steep trajectory of 51 ◦ to the horizon from 81 km down to 19 km altitude, where the luminousflight ended at a speed of 3.2 km s − . Deceleration data indicated one large fragment had made it to the ground. Thefour person search team recovered a 1.15 kg meteorite within 130 m of the predicted fall line, after 8 hours of searching,6 days after the fall. Dingle Dell is the fourth meteorite recovered by the DFN in Australia, but the first before anyrain had contaminated the sample. By numerical integration over 1 Ma, we show that Dingle Dell was most likelyejected from the main belt by the 3:1 mean-motion resonance with Jupiter, with only a marginal chance that it camefrom the nu resonance. This makes the connection of Dingle Dell to the Flora family (currently thought to be theorigin of LL chondrites) unlikely. Corresponding author: Hadrien A. R. [email protected] a r X i v : . [ a s t r o - ph . E P ] A p r Devillepoix et al. INTRODUCTIONAs of mid-2017 there are nearly 60k meteorite samples classified in the Meteoritical Bulletin Database ∗ . However,apart from a handful of Lunar ( (cid:39) (cid:39) Dedicated networks to recover meteorites with known provenance
In the decade following 2000, the recovery rate of meteorites with determined orbits has dramatically increased(Boroviˇcka et al. 2015), without a significant increase in collecting area of the major dedicated fireball networks.While the initial phase of the Desert Fireball Network (DFN) started science operations in December 2005, covering0 . × km (Bland et al. 2012), other major networks ceased operations. The Prairie network in the USA (0 . × km (McCrosky and Boeschenstein 1965)) shut down in 1975, the Canadian Meteorite Observation and RecoveryProject (MORP) - 1 . × km - stopped observing in 1985 (Halliday et al. 1996), and the European Network’scovering area of ∼ × km has not significantly changed (Oberst et al. 1998). If not due to a larger collecting area,this increase can be explained by other factors: • Existing networks improving their data reduction techniques (Spurn´y et al. 2014). • Democratisation and cheap operating cost of recording devices (surveillance cameras, consumer digital cameras...)(Boroviˇcka et al. 2003). • Use of doppler radar designed for weather observations to constrain the location of falling meteorites (Jenniskenset al. 2012; Fries et al. 2014; Fries and Fries 2010). • Deployment of the Desert Fireball Network expressly on favourable terrain to search for meteorites. In its earlystage, within its first 5 years of science operation, the DFN yielded 2 meteorites (Bland et al. 2009; Spurn´y et al.2011), whilst MORP only yielded one (Halliday et al. 1981) in 15 years of operations over a larger network. • To a lesser extent, development of NEO telescopic surveillance programmes. One single case so far (the CatalinaSky Survey detecting the Almahata Sita meteoroid several hours before impact Jenniskens et al. (2009)), howeverthis technique is likely to yield more frequent successes with new deeper and faster optical surveyors, like LSST,which comes online in 2021 (Ivezic et al. 2008).The DFN started developing digital observatories to replace the film based network in 2012 with the goal of covering10 km , the more cost effective than expected digital observatories allowed the construction of a continent-scalenetwork covering over 2 . × km (Howie et al. 2017a). This programme rapidly yielded results, less than a yearafter starting science operation (in November 2014). One of the observatories lent to the SETI institute in Californiawas a crucial viewpoint to calculating an orbit for the Creston fall in California in October 2015 (Met 2015), and thefirst domestic success came 2 months later with the Murrili meteorite recovery on Kati Thanda–Lake Eyre (Devillepoixet al. 2016; Met 2016). We report here the analysis of observations of a bright fireball that led to the fourth find bythe Desert Fireball Network in Australia: the Dingle Dell meteorite. Dingle Dell was originally classified as an LLordinary chondrite, petrographic type 6 (Met 2017). However, further analysis revealed that it in fact sits on the L/LL ∗ ASTEX sample article
Current understanding of the origin of the main groups of L and LL chondrites
L chondrites — L chondrites represent 32% of total falls. Schmitz et al. (2001) first identified a large amount of fossilL chondrites meteorites in (cid:39)
467 Ma sedimentary rock, which suggests that a break up happened not too long before,near an efficient meteorite transport route. From spectroscopic and dynamical arguments, Nesvorn´y et al. (2009)proposed that the Gefion family break up event, close to the 5:2 MMR with Jupiter, might be the source of thisbombardment, given the rapid delivery time, and a likely origin of L chondrite asteroids outside of the 2.5 AU. Mostshocked L5 and L6 instrumentally observed falls also seem to come from this break up, with an Ar − Ar age around (cid:39)
470 Ma ago: Park Forest (Brown et al. 2004), Novato (Jenniskens et al. 2014), Jesenice (Spurn´y et al. 2010), andInnisfree (Halliday et al. 1981). Only the Villalbeto de la Pe˜na L6 (Trigo-Rodr´ıguez et al. 2006) does not fit in thisstory because of its large cosmic ray exposure age (48 Ma), inconsistent with a 8.9 Ma collisional lifetime (Jenniskens2014).
LL chondrites — Thanks to Vernazza et al. (2008), we know that S- and Q-type asteroids observed in NEO spaceare the most likely asteroidal analogue to LL type ordinary chondrites. The Hayabusa probe returned samples fromS-type (25143) Itokawa, finally unequivocally matching the largest group of meteorites recovered on Earth (ordinarychondrites) with the most common spectral class of asteroids in the main belt (Nakamura et al. 2011). The samplebrought back from Itokawa is compatible with LL chondrites. Indeed, LL compatible asteroids make up two thirdsof near-Earth space. The spectrally compatible Flora family from the inner main belt can regenerate this populationthrough the ν secular resonance. But one large problem remains: only 8% of falls are LL chondrites (Vernazza et al.2008). The orbits determined for some LL samples have so far not helped solve this issue. If we exclude Beneˇsov(Spurn´y et al. 2014), which was a mixed fall, scientists had to wait until 2013 to get an LL sample with a preciselycalculated orbit: Chelyabinsk (Brown et al. 2013; Boroviˇcka et al. 2013). The pre-atmospheric orbit and compositionof the Chelyabinsk meteorite seems to support the Flora family origin for LL chondrites, although a more recentimpact could have reset the cosmic ray exposure age to 1 . ± . (cid:39) m ), and the presence of impact melts.Based on it’s classification, we put the orbit of the Dingle Dell meteorite in context with other calculated orbits fromL and LL chondrites and discuss the resonances from which it may have originated. FIREBALL OBSERVATION AND TRAJECTORY DATAOn Halloween night shortly after 8 PM local time, several reports of a large bolide were made via the
Fireballs In TheSky smart-phone app (Sansom et al. 2016) from the Western Australian Wheatbelt area. These were received a fewhours prior to the daily DFN observatory reports, apprising the team of the event expeditiously. The DFN observatorysightings are routinely emailed after event detection has been completed on the nights’ data-set. It revealed that sixnearby DFN observatories simultaneously imaged a long fireball starting at 12:03:47.726 UTC on October 31, 2016(Figure 1). 2.1.
Instrumental records
The main imaging system of the DFN fireball observatories is a 36 MPixel sensor: Nikon D810 (or D800E on oldermodels), combined with a Samyang lens 8mm F/3.5. Long exposure images are taken every 30 seconds. The absoluteand relative timing (from which the fireball velocity is derived) is embedded into the luminous trail by use of a liquidcrystal (LC) shutter between the lens and the sensor, modulated according to a de-Brujin sequence (Howie et al. 2017b).The LC shutter operation is tightly regulated by a micro-controller synced with a Global Navigation Satellite System
Devillepoix et al.
Figure 1.
Cropped all-sky images of the fireball from the six DFN observatories. Images are of the same pixel scale withthe centre of each image positioned at the observatory location on the map (with the exception of Perenjori, whose location isindicated). The Badgingarra image is cropped because the sensor is not large enough to accommodate the full image circle on itsshort side. The saturation issue is exacerbated by light scattered in the clouds on cameras close to the event, this is particularlyvisible on the Perenjori image. The black blotch in the Perenjori image is an artefact that thankfully did not extend far enoughto affect the quality of the data. Approximate trajectory path shown by orange arrow. Location of the recovered meteorite isshown by the red dot. (GNSS) module to ensure absolute timing accurate to ± . ASTEX sample article
5a Fujinon fisheye lens. Originally intended as a backup device for absolute timing, these video systems have beenretained for future daytime observation capabilities. Here we make use of the video data to acquire a light curve,as the event saturated the still camera sensors. The closest camera system to this event was in Perenjori (Table 1),located almost directly under the fireball, and was the only station to image the end of the luminous trajectory (Fig.1). Other nearby camera sites were overcast and did not record the event. In order to triangulate the trajectory ofthe fireball, distant stations had to be used, all over 200 km away. The Hyden, Kukerin and Newdegate systems wereall around 500 km from the event and, although still managing to capture the fireball, were too low on the horizon foraccurate calibration.
Table 1.
Locations and nature of instrumental records. We use cameras < km away for trajectory determination.Observatory Instruments Latitude Longitude altitude (m) distance * (km)Perenjori P, V 29.36908 S 116.40654 E 242 91Badgingarra P 30.40259 S 115.55077 E 230 204Northam P 31.66738 S 116.66571 E 190 323Hyden P 32.40655 S 119.15325 E 390 484Kukerin P 33.25337 S 118.00628 E 340 520Newdegate P 33.05436 S 118.93534 E 302 534P: Photographic record (exposures: 25 seconds, 6400 ISO, F/4.), V: video record ∗ distance from the meteoroid at 70 km altitude Astrometry
All images captured by the DFN observatories are saved even when no fireball is detected. This is possible thanksto the availability of large capacity hard drives at reasonable costs. Not only does this mitigate event loss duringinitial testing of detection algorithms, but it gives a snapshot of the whole visible sky down to 7.5 point source limitingmagnitude, every 30 seconds. The astrometric calibration allows the points picked along the fireball image to beconverted to astrometric sky coordinates. The associated astrometric uncertainties are dominated by the uncertaintyon identifying the centroids along the segmented fireball track.We have carried out studies on the long-term camera stability by checking the camera pointing using astrometry.On the outback system tested, the pointing changed less than 1 (cid:48) over the 3 month period assessed. The pointing istherefore remarkably stable, and the relevant fireball image can thus be astrometrically calibrated using a picture takenat a different epoch. This is particularly useful when a bright fireball overprints nearby stars, and especially in thiscase where clouds are present. In general however, we aim to use a calibration frame taken as close as possible fromthe science frame, particularly when studying an important event, such as a meteorite fall. In the following paragraphwe present the methods used for astrometrically calibrating the still images, using as an example the Perenjori data.This technique is implemented in an automated way in the reduction pipeline for all detected events.The astrometric solution for the Perenjori camera is obtained using an image taken a few hours after the event, oncethe clouds had cleared (2016-10-31T16:00:30 UTC), containing 1174 stars of apparent magnitude m V ∈ [1 . , . rd order polynomial fit is performed to match detected stars to the Tycho-2 star catalogue. The transformation isfurther corrected using a 2 nd order polynomial on the radial component of the optics. The stability of the solutioncan be checked at regular intervals. The slight degradation in altitude precision for altitudes below 20 ◦ in Fig. 3, isdue to a partly obstructed horizon from this camera (eg. trees, roofs). This degradation usually starts around 10 ◦ oncameras with a clear horizon, as is the case for most outback systems.The beginning of the fireball on the Perenjori image is partially masked by clouds, yielding only a handful of points.The middle section is not usable as the sensor was saturated in large blobs, rendering impossible timing decoding oreven reliable identification of the centre of the track. However the Perenjori image provides a good viewpoint for theend of the fireball.Well calibrated data were also obtained from the Badgingarra camera, before it went outside the sensor area at30.6 km altitude. Although the Northam camera was very cloudy, we were able to pick the track of the main meteoroid Devillepoix et al.
Figure 2.
Configuration of DFN station observations for the Dingle Dell fireball. White rays show observations used intriangulation of the trajectory (approximated to the yellow line, starting NE and terminating to the SW of Perenjori). Hyden,Newdegate and Kukerin stations were all around 500 km away from the event and were not used in triangulation. body without timing information, and use it as a purely geometric constraint. Hyden, Kukerin, and Newdegate alsopicked up the fireball, however the astrometry so low on the horizon ( < ◦ ) was too imprecise (between 2 and 4arcminutes) to refine the trajectory solution. 2.3. Photometry
The automated DFN data reduction pipeline routinely calculates brightness for non-saturated fireball segments.For this bright event however, the brightness issue was exacerbated by large amounts of light scattered in the clouds(Fig. 1), so it was impossible to produce a useful light curve from the photograph. On the other hand, the Perenjoriobservatory recorded a low-resolution compressed video through the clouds. Although it is not possible to calibratethis signal, we can get a remarkably deep dynamic range reading of the all-sky brightness, thanks to the large amountof light scattered in the numerous clouds. By de-interlacing the analogue video frames, we were able to effectivelydouble the time resolution (25 interlaced frames per second to 50 fields per second, which are equally as precise forall-sky brightness measurements). To correct how the auto-gain affects the signal, we perform aperture photometryon Venus throughout the event. The analogue video feed is converted to digital by the Commell MPX-885 capturecard, and then processed by the compression algorithm (H264 VBR, FFmpeg ultrafast preset) (Howie et al. 2017a)before being written to disk, divided into 1 minute long segments. The PC clock is maintained by the Network TimeProtocol (NTP) service, fed with both GNSS and network time sources. However the timestamp on the file created bythe PC suffers from a delay. We measured the average delay using a GPS video time inserter (IOTA-VTI) on a testobservatory. This allowed us to match the light curve obtained from the video to astrometric data to within 20 ms.Peak A in Figure 4 is visible on the photographs from both Badgingarra and Hyden. These are used to validate theabsolute timing alignment of the video data. ASTEX sample article
300 200 100 0 100 200 300 azimuth residuals (arcsec) x cos(elevation) e l e v a t i on r e s i dua l s ( a r cs e c ) Figure 3.
Residuals on the global astrometric solution for the Perenjori camera. The pixel size at the centre of the FoV isshown by the grey square in order to gauge the quality of the solution, as well as the 1 σ residual bars on the stars. The azimuthresiduals are artificially large around the pole of the spherical coordinate system, so we have multiplied them by cos ( elevation )to cancel out this artefact. Eye witnesses
Three anecdotal reports of the fireball were received via the
Fireballs in the Sky smartphone app (Paxman andBland 2014; Sansom et al. 2016) within two hours of the event (Table 2). The free app is designed to enable membersof the public to easily report fireball sightings. Phone GPS, compass, and accelerometers are utilised to report thedirection of observations, while a fireball animation aids users in estimating the colour, duration and brightness of
Devillepoix et al. A ll - sky b r i gh t ne ss ( a r b i t r a r y un i t ) A B C D E F
Figure 4.
All-sky brightness (sum of all the pixels) from the video camera at the Perenjori observatory. The light curve iscorrected to take into account the effect of auto-gain.Reporting Report Location Approx. Distance Reported Reported ReportedMeans Time From Event Duration (s) Brightness Colour(UTC) (km) (stellar Mag)FITS 12:04 Perth 300 2.6 -8 OrangeregionFITS 12:59 Ballidu 150 6.4 -7 GreenFITS 13:35 Dowerin 230 8.6 -9 Pinkeye N/A Koolanooka 7.4 > > − . Table 2.
Observer reports from eyewitness accounts and
Fireballs in the Sky app (FITS). the event. This app is an interactive alternative to the popular web-based reporting tool of the International MeteorOrganisation (Hankey and Perlerin 2015).The app reports were the first notification of the fireball received by the DFN team, even before the receipt of dailyemails from the fireball observatories. The azimuth angles reported by the observers were not sufficiently consistentto enable a triangulation based on app reports alone.The fireball was also reported by several nearby witnesses, and was described in detail by an eye witness only 7 . FIREBALL TRAJECTORY ANALYSIS3.1.
Geometry
ASTEX sample article ◦ . The trajectory solution points to a moderately steep entry with a slope of 51 ◦ from thehorizon, with ablation starting at an altitude of 80 . . c r o ss - t r a ck r e s i dua l s ( m ) Perenjori - [98-36] kmBadgingarra - [216-163] kmNortham - [332-294] km
Figure 5.
Cross-track residuals of the straight line least squares fit to the trajectory from each view point. These distancescorrespond to astrometric residuals projected on a perpendicular plane to the line of sight, positive when the line of sightfalls above the trajectory solution. Note that the larger residuals on the Northam camera do not equate to larger astrometricuncertainties, but rather reflect a rather large distance from the observatory. The distances in the legend correspond to theobservation range [highest point - lowest point].
Dynamic modelling of the trajectory, including velocity and mass determination
Filter Modelling — The method described in Chapter 4 of Sansom (2016) is an iterative Monte Carlo technique thataims to determine the path and physical characteristics such as shape ( A : the cross section area to volume ratio),density ( ρ m ), and ablation coefficient ( σ ) of a meteoroid from camera network data. In this approach, one is ableto model meteoroid trajectories based on raw astrometric data. This avoids any preconceived constraints imposedon the trajectory, such as the straight line assumption used in Section 3.1. Unfortunately this requires multiple viewpoints with accurate absolute timing information to record the meteoroid position. For this event, timings encoded inthe trajectory were distinguishable for only the initial 4 . Devillepoix et al.
Event Time * Speed Height Longitude Latitude Dynamic pressures m s − m ◦ E ◦ N MPaBeginning 0.0 15443 ±
60 80594 116.41678 -28.77573A 1.20 15428 65819 116.36429 -28.86973 0.03B 1.72 15401 59444 116.34151 -28.91045 0.08C 1.96 15378 56531 116.33108 -28.92909 0.11D 4.08 13240 32036 116.24270 -29.08672 2.28E 4.58 10508 27302 116.22547 -29.11738 3.09F 4.84 8988 25019 116.21716 -29.13217 3.27Terminal 6.10 3243 ±
465 19122 116.19564 -29.17045 ∗ past 2016-10-31T12:03:47.726 UTC Table 3.
Summary table of bright flight events. Fragmentation event letters are defined on the light curve (Fig. 4) three dimensional particle filter model outlined in Chapter 4 of Sansom (2016) using instead triangulated geocentriccoordinates as observation measurements. Uncertainties associated with using pre-triangulated positions based on anassumed straight line trajectory are incorporated. The distribution of particle positions using such observations willbe overall greater than if we had been able to use the raw measurements.As a straight line may be an oversimplification of the trajectory, to most reliably triangulate the end of the lu-minous flight using the SLLS method, the final 1.1 seconds were isolated (this being after all major fragmentationevents described in Section 3.3). The filter was run using these positions and initiated at t = 5 . σ , and shape density coefficient, κ . At the final observation time t f = 6 . mass f = 1 . ± .
23 kg, speed f =3359 ±
72 m s − , σ f = 0 . ± . s km − and κ f = 0 . ± . κ may be used to calculatedensities for a given shape and drag coefficient, to avoid introducing assumptions at this stage we may gauge its valueby reviewing the density with which surviving particles were initiated. The distribution of final mass estimates isplotted against this initial density attributed to each given particle in Figure 7, along with the recovered Dingle Dellmeteorite mass of 1 .
150 kg and bulk density of 3450 kg m − . In this figure, the distribution of the main cluster ofparticles is consistent with the recovered mass, however the initial densities are lower. The weighted median valueof initial bulk densities (at t = 5 . t f is 3306 kg m − . It is expected that the bulkdensity of a meteoroid body may slightly increase throughout the trajectory as lower density, more friable material ispreferentially lost. This could justify the slightly lower bulk densities attributed at t .In order to obtain the entry speed of the meteoroid with appropriate errors, we apply an extended Kalman smoother(Sansom et al. 2015) to the straight line solution for the geometry, considering the timing of the points independently ASTEX sample article Figure 6.
Position residuals of the 3D particle filter fit to the SLLS triangulated observations for the final 1 . s of the luminoustrajectory. Individual particle weightings are shown in greyscales, with weighted mean values shown in red. for each observatory. Of the two cameras that have timing data for the beginning of the trajectory, only Badgingarracaught the start, giving an entry speed of 15402 ±
60 m s − (1 σ ) at 80596 m altitude. To determine whether speedscalculated are consistent between observatories, the first speed calculated for Perenjori – 15384 ±
64 m s − at 75548 maltiude – is compared to the Badgingarra solution at this same altitude –15386 ±
43 m s − . The results are remarkablyconsistent, validating the use of a Kalman smoother for determining initial velocities. Dimensionless Coefficient Method — As a comparison to the particle filter method, the dimensionless parameter techniquedescribed by Gritsevich (2009) was also applied. The ballistic parameter ( α ) and the mass loss parameter ( β ) werecalculated for the event, resulting in α = 9 .
283 and β = 1 .
416 (Figure 8). As the particle filter technique in this casewas not able to be performed on the first 5.0 seconds of the luminous trajectory, these parameters may be used todetermine both initial ∗ , and final † main masses, given assumed values of the shape and density of the body. Usingthe same parameters as Gritsevich (2009) ( c d = 1, A = 1 .
55) along with the density of the recovered meteorite, ρ = 3450 kg m − , gives an entry mass, m e = 81 . m f = 1 . A = 1 .
21 (Bronshten 1983) gives an initial mass of m e = 38 . c d and A , wecan also insert the κ value calculated by the particle filter to give m e = 41 . . . Atmospheric behaviour
In Table 3 we report the ram pressure ( P = ρ a v ) required to initiate the major fragmentation events labelled onthe light curve in Fig. 4. The density of the atmosphere, ρ a , is calculated using the NRLMSISE-00 model of Piconeet al. (2002), and v is the calculated speed. The meteoroid started fragmenting quite early (events A , B , and C ),starting at 0 .
03 MPa. These early fragmentation events suggest that the meteoroid had a much weaker lithology thanthe meteorite that was recovered on the ground. Then no major fragmentation happened until two very bright peaks ∗ see equation 14 in Gritsevich (2009) † see equation 6 in Gritsevich (2009) Devillepoix et al. kg )200022502500275030003250350037504000 D e n s i t y ( k g . m ) w e i g h t e d p a r t i c l e s Figure 7.
Results of the 3D particle filter modelling, showing the distribution of final mass estimates along with the densitieswith which particles were initiated at t = 5 s. Mass estimates are consistent with the recovered meteorite mass found (redcross), with initial densities slightly below the bulk rock value. in the light curve: D (2 .
28 MPa) and E (3 .
09 MPa). These large short-lived peaks suggest a release of a large numberof small pieces that quickly burnt up. A small final flare ( F –3 .
27 MPa) 1.26 second before the end is also noted. DARK FLIGHT AND METEORITE RECOVERYThe results of the dynamic modelling (Fig. 7) are fed directly into the dark flight routine. By using the state vectors(both dynamical and physical parameters) from the cloud of possible particles, we ensure that there is no discontinuitybetween the bright flight and the dark flight, and we get a simulation of possible impact points on the ground that isrepresentative of the modelling work done on bright flight data.4.1.
Wind modelling
The atmospheric winds were numerically modelled using the Weather Research and Forecasting (WRF) softwarepackage version 3.8.1 with the Advanced Research WRF (ARW) dynamic solver (Skamarock et al. 2008). The weathermodelling was initialised using global 1 ◦ resolution National Centers for Environmental Prediction (NCEP) Final anal-ysis (FNL) Operational Model Global Tropospheric Analysis data. As a result, a 3 km resolution WRF product with30 minutes history interval was created and weather profile at the end of the luminous flight for 2016-10-31T12:00 UTCextracted (Fig. 9). The weather profile includes wind speed, wind direction, pressure, temperature and relative humid-ity at heights ranging up to 30 km (Fig. 9), providing complete atmospheric data for the main mass from the end of theluminous phase to the ground, as well as for fragmentation events E and F (Table 3). Different wind profiles have beengenerated, by starting the WRF integration at different times: 2016 October 30d12h, 30d18h, 31d00h, 31d06h, and31d12h UTC. Three of the resulting wind models converge to a similar solution in both speed and direction (30d12h,31d00h, 31d12h) and will be hereafter referred to as solution W1 (Fig. 9). The other two models from 30d18h ( W2 ) ASTEX sample article Figure 8.
Trajectory data from both Perenjori and Badgingarra observatories, with speeds normalised to the speed at thetop of the atmosphere (15.443 km s − ; Tab. 3), V , and altitudes normalised to the atmospheric scale height, h = 7 .
16 km.The best fit to Equation 10 of Gritsevich (2009) results in α = 9 .
283 and β = 1 .
416 and is shown by the blue line. Thesedimensionless parameters can be used to determine the entry and terminal mass of the Dingle Dell meteoroid. and 31d00h ( W3 ) differ significantly. For example, the maximum jet stream strength is (cid:39)
47 m s − for W1 , (cid:39)
34 m s − for W3 , and (cid:39)
29 m s − for W2 . To discriminate which wind profile is closer to the truth, we ran the model next tothe Geraldton balloon launches of 2016 October 31d00h and 31d06h UTC, but no discrepancy was noticeable betweenall 5 scenarios. Considering that 3 model runs clump around W1 , whereas W3 and W2 are isolated, we choose W1 asa preferred solution. The investigation of why W3 and W2 are different is beyond the scope of this paper, nonethelesswe discuss how these differences affect the dark flight of the meteorites in the next section (4.2).4.2. Dark flight integration
The calculations of the unobserved terminal part of the ablation phase and the dark flight are performed using an 8 th order explicit Runge-Kutta integrator with adaptive step-size control for error handling. The physical model uses thesingle body equations for meteoroid deceleration and ablation (Hoppe 1937; Whipple 1939). In this model, rotation isaccounted for such that the cross sectional area to volume ratio ( A ) remains constant throughout the trajectory. Thevariation in flow regimes and Mach ranges passed through by the body alter the values used for the drag coefficient,which can be approximated using Table 1 in (Sansom et al. 2015).The integration of all the particles from Section 3.2 allows the generation of probability heat maps to maximise fieldsearching efficiency. The ground impact speed for the mass corresponding to the recovered meteorite is evaluated at67 m s − .4 Devillepoix et al.
NW S E0 10 20Altitude (km ASL) 01020304050 W i n d s p ee d ( m s ) Figure 9.
Wind model profile W1 , extracted as a vertical profile at the coordinates of the lowest visible bright flight measure-ment. In calculating a fall line for an arbitrary range of masses, the assumed shape of the body and the wind model usedboth affect the final fall position. However for a given wind model a change in shape only shifts the masses along thefall line.We also calculate dark flight fall lines from fragmentation events that happened within the wind model domain: E and F . Unsurprisingly, the main masses from those events are a close match to the corresponding main mass startedfrom the end of the visible bright flight. However small fragments are unlikely to be found as they fell into theKoolanooka Hills bush land (Fig. 10). 4.3. Search and recovery
Within two days, two of the authors (PB and MT) visited the predicted fall area, about 4 hours’ drive from Perth,Western Australia to canvas local farmers for access and information. Having gained landowner permission to search,a team was sent to the area 3 days later. Searching was carried out by a team of 4 (MT, BH, TJS, and HD), mostlyon foot and with some use of mountain biking in open fields. The open fields’ searching conditions were excellent,although the field boundaries were vegetated. The team managed to cover about 12 ha per hour when looking for a > λ = 116 . ◦ φ = − . ◦ (WGS84), about 130 m from the originally calculated fall line, after atotal of 8 hours of searching. The recovered meteorite weighs 1.15 kg, with a rounded brick shape of approximately16 x 9 x 4 cm, and a calculated bulk density of 3450 kg m − (Fig. 11). The condition of the meteorite is excellent,having only been on ground for 6 days, 16 hours. Discussion with the local landowner, and checking the weather onthe nearest Bureau Of Meteorology observation station (Morawa Airport, 20 km away) showed that no precipitationhad fallen between times of landing and recovery. The meteorite was collected and stored using a Teflon bag, and localsoil samples were also collected in the same manner for comparison. No trace of impact on the ground was noticed. ASTEX sample article Figure 10.
Fall area around Dingle Dell farm and Koolanooka Hills. Fall lines in yellow represent different wind model solutions: W1 (bottom), W2 (middle) and W3 (top). Mass predictions for the preferred wind model are shown for spherical (light bluemarkings; A = 1 .
21) and cylindrical (white markings; A = 1 .
5) assumptions. The particle filter results are propagated throughdark flight using wind model W1 , and are shown as a heat map. The location of the recovered meteorite (red dot) is (cid:39)
100 mfrom the W1 fall line. The meteorite was found intact (entirely covered by fusion crust) on hard ground, resting up-right (Fig. 11), andcovered with dust. So it is possible that the meteorite fell a few metres away in softer ground and bounced or rolledto the recovered position. PRE-ENCOUNTER ORBITThe backward propagation of the observed trajectory into an orbit requires the calculation of the direction of thefireball (known as the radiant), and the position and speed at the top of the atmosphere. The associated uncertaintieson these two components are mostly un-correlated. In order to minimise issues associated with the oversimplifiedstraight line trajectory for orbit purposes, we re-triangulate the observations using only points that fall >
60 kmaltitude on the initial triangulation. In this case, as the trajectory is fairly steep, the difference in apparent radiantbetween the two solutions is less than 5 arcmin. To calculate the errors on the radiant, we use the co-variance matrixfrom the least squares trajectory fit (see section 3.1), this gives us the apparent radiant: slope to the horizontal =51 . ± . ◦ , azimuth of the radiant (East of North) = 26 . ± . ◦ , which corresponds to ( α = 353 . ± . ◦ , δ = 6 . ± . ◦ ) in equatorial J2000 coordinates.To calculate the formal uncertainty on the initial velocity, we apply the Kalman filter methods of Sansom et al. (2015)as outlined in Section 3.2. Using the time, position, radiant, speed, and their associated uncertainties, we determinethe pre-atmospheric orbit by propagating the meteoroid trajectory back through time, considering the atmosphericdeceleration, Earth’s oblate shape effects (J2), and other major perturbing bodies (such as the Moon and planets),until the meteoroid has gone beyond 10 × the Earth’s sphere of influence. From here, the meteoroid is propagatedforward in time to the impact epoch, ignoring the effects of the Earth-Moon system. Uncertainties (Table 4) are6 Devillepoix et al.
Figure 11.
Dingle Dell meteorite as it was found. Image available at https://commons.wikimedia.org/wiki/File:Dingle_Dell_meteorite_as_it_was_found.jpg under a Creative Commons Attribution-ShareAlike 4.0 International. calculated using a Monte Carlo approach on 1000 test particles randomly drawn using uncertainties on the radiantand the speed.We scanned the
Astorb ‡ asteroid orbital database (Bowell et al. 2002) for close matches in a, e, i, ω, Ω orbital spaceusing the similarity criterion of Southworth and Hawkins (1963). The closest match is the small ( H = 24 . ‡ ftp://ftp.lowell.edu/pub/elgb/astorb.html , downloaded June 24, 2017 ASTEX sample article -5-4-3-2-1 1 2-1 1 2 AU ♈ Dingle DellInner planetsJupiter
Figure 12.
Ecliptic projection of the pre-encounter orbit of Dingle Dell. The shades of grey represent the likelihood ascalculated from 1000 Monte Carlo simulations based on formal uncertainties on the radiant and the speed. asteroid, that came into light in November 2015 when it flew by Earth at (cid:39)
10 lunar distances. But the large differencebetween these orbits, D = 0 .
04, makes the dynamical connection between the two highly unlikely.To calculate the likely source region and dynamical pathway that put the meteoroid on an Earth crossing orbit, weuse the
Rebound integrator (Rein and Tamayo 2015) to backward propagate the orbit of the meteoroid. We use 10,000test particles randomly selected using the radiant and speed uncertainties as explained above, as well as the majorperturbating bodies (Sun, 8 planets, and Moon). The initial semi-major axis (Table 4) is close to the 7:2 (2.25 AU) and10:3 (2.33 AU) mean motion resonances with Jupiter (MMRJ). These minor resonances start to scatter the eccentricityof a large number of test particles very early on, but neither are strong enough to decrease it significantly enough totake the meteoroid outside of Mars’ orbit. Because of the interactions with the inner planets, the particle cloud rapidlyspreads out, and particles gradually start falling into the two main dynamical pathways in this region: 3:1 MMRJ8
Devillepoix et al.
Epoch TDB 2016-10-31 a AU 2.254 ± e ± i ◦ ± ω ◦ ± ◦ ± q AU 0.92328 ± Q AU 3.586 ± α g ◦ ± δ g ◦ ± V g m s − ± T J Table 4.
Pre-encounter orbital parameters expressed in the heliocentric ecliptic frame (
J2000 ) and associated 1 σ formaluncertainties. (2.5 AU) and the ν secular resonance. These resonances rapidly expand the perihelia of particles out of the Earth’sorbit initially, and eventually out of Mars’ orbit and into the main belt.During the integration over 1 million years, we count the number of particles that have converged close to stablypopulated regions of the main belt, and note which dynamical pathway they used to get there. This gives us thefollowing statistics: • ν : 12% • • CONCLUSIONSDingle Dell is the fourth meteorite with an orbit recovered by the DFN in Australia. Its luminous trajectory wasobserved by 6 DFN camera stations up to 535 km away at 12:03:47.726 UTC on 31 October, 2016. Clouds severelyaffected the observations, but enough data was available to constrain the search area for a swift recovery, and determineone of the most precise orbits linked to a meteorite. The surviving rock was recovered within a week of its fall, withoutany precipitation contaminating the rock, confirming the DFN as a proficient sample recovery tool for planetaryscience. This recovery, in less than ideal conditions, also validates various choices in the design and operations of theDesert Fireball Network: • Use of high resolution digital cameras to enable reliable all-sky astrometry for events up to 300 km away. • Uninterrupted operation even when a large portion of the sky is cloudy for individual systems. • Archiving of all raw data to mitigate event detection failures.While the method of Sansom et al. (2017) was still in development at the time of the fall, the re-analysis of thefireball with this new technique is remarkably consistent with the main mass found, requiring just a small number ofhigh quality astrometric data points. This validates the method, and will drastically reduce the search area for futureobserved falls.After a 1 million year integration of 10,000 test particles, it is most likely that Dingle Dell was ejected from themain belt through the 3:1 mean motion resonance with Jupiter rather than the ν resonance (82% for the 3:1 MMRJcompared to 12% probability for ν ). This also means that L/LL Dingle Dell is unlikely to be associated with theFlora family, likely source of most LL chondrites (Vernazza et al. 2008), as the most efficient mechanism for gettingFlorian fragments to near-Earth space is the ν secular resonance. This fall adds little insight into the Flora/LL link, ASTEX sample article -4-2024-4 -2 0 2 4 AU ♈ AU L chondritesLL chondrites Dingle DellPlanets
Figure 13.
The orbit of Dingle Dell in context with other L and LL ordinary chondrite falls. References for L and LL orbitsare in the introduction. but 2016 was rich in instrumentally observed LL falls, which might yield clues to help confirm this connection inthe near future: Stubenberg (LL6) (Spurn´y et al. 2016; Bischoff et al. 2017), Hradec Kr´alov´e (LL5) (Met 2017), andDishchii’bikoh (LL7) (Met 2017; Palotai et al. 2018).ACKNOWLEDGEMENTSThe authors would like to thank the people hosting the observatories, members of the public reporting their sightingsthrough the
Fireballs In The Sky program, and other volunteers that have made this project possible. This researchwas supported by the Australian Research Council through the Australian Laureate Fellowships scheme and receivesinstitutional support from Curtin University. The DFN data reduction pipeline makes intensive use of Astropy, acommunity-developed core Python package for Astronomy (Astropy Collaboration et al. 2013). The authors wouldalso like to thank J. Boroviˇcka and J. Vaubaillon for their valuable comments and suggestions, which improved thequality of the paper. APPENDIX A. SUPPLEMENTARY FILESWe provide the raw astrometric tables for the 3 cameras used for computing the trajectory.We also give the straight line trajectory solution (latitude, longitude, height), as well as the corresponding speedscalculated by the method of Sansom et al. (2015) using all the data available (this explains slight differences with themanuscript, as in the latter they were calculated separately for each camera).0
Devillepoix et al.
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