SB11

The Desert Fireball Network is a fireball observing network which stretches across the southern part of the Australian continent. To date, it has over 50 cameras, covering an area of approximately 2.5m km2. Its purpose is to observe and triangulate fireballs, calculate trajectories for incoming meteorites. The camera network has been operational in digital form since 2012, and to date as captured approximately 1.5PTB of data, primarily all sky images. We present an overview of the DFN results to date, detailing the dataset of approximately 1500 orbits, and over 30 possible candidate meteorite falls, and describe the most recent results. In particular, the team have recently recovered two candidate meteorites; one from the Nullarbor and one from the Simpson Desert in South Australia. The comparison the stories of these recoveries illustrate the typical issues of searching meteorite searching, and of verifying the meteorite’s provenance, and possible origin of the rocks is interesting to compare.

How to cite: Towner, M., Sansom, E., Cupak, M., Devillepoix, H., Anderson, S., Shober, P., Howie, R., Hartig, B., and Bland, P.: The Australian Desert Fireball Network: overview and recent results, Europlanet Science Congress 2021, online, 13–24 Sep 2021, EPSC2021-167, https://doi.org/10.5194/epsc2021-167, 2021.

• Introduction

In Spain, the Spanish Meteor Network (SPMN) has been operating for 25 years, recording meteoric events and re-entries over the Iberian Peninsula, Morocco, and the insular territory [1]. This is a pro-am project involving a scientific team specialized in areas such as astronomy, geology, geophysics, and chemistry.

Obtaining the trajectory of meteoroids impacting the atmosphere is crucial both for the recovery of possible meteorites and for studying their origin in the Solar System. Some of these objects can be dynamically associated with their parent bodies, being part of meteoroid streams [2]. Herein lies the importance of monitoring the sky constantly and completely from multiple monitoring stations. The SPMN network has 34 stations equipped with all-sky cameras or wide-angle lenses, and we recently upgraded the software to reduce the ever-increasing amount of data. Here we present our automated Python (called 3D-FireTOC) pipeline for meteor detection from digital systems, astrometric measurements, photometry, atmospheric trajectory reconstruction and heliocentric orbit computation, all in all quantifying the error measurements in each step [3].

• Analytical procedures of the 3D-FireTOC software

A key step to achieve proper reduction is the development of automated astrometry to ensure the measurement of meteors appearing in the field of view of video-detection systems. To do this, we use computer vision techniques to obtain the pixel coordinates corresponding to the moving meteor in each frame. Each image is processed and compared with a reference image (without detection) allowing us to extract the pixels that have been activated by the meteor. In this way, the centroid of the detected pixel area corresponds to the position in the image of the meteoroid (see Figure 1).

Due to the changing nature of this type of recordings as well as possible light reflections and obstacles in the field of view, we have implemented three methods to avoid false positives: 1) discriminating by the size of the detected area excluding excessively small and large contours, 2) predicting the next position of the meteor with a Kalman filter, and 3) post-processing the detected points and applying clustering algorithms to check if the trajectory is consistent with a more or less straight line. Figure 2 shows an example of false positive avoidance.

To transform the pixels into real coordinates is necessary to identify stars in the image to obtain their position in the sky for the date of the event. To do this, we use corner detection algorithms since the stars appear randomly distributed in the sky and far from each other. Again, we use clustering algorithms but this time selecting the points identified as noise, as can be seen in Figure 2.

Once the stars have been identified, thanks to JPL's Horizons ephemerides, we can model the deformation produced by the lens by finding the correspondence between pixel and real position. In particular, we apply a polynomial variant [4] of the method proposed by [5] for all-sky camera astrometry. 

Fig.1