The discovery of the first electromagnetic counterpart to a gravitational wave signal has generated follow-up observations by over 50 facilities world-wide, ushering in the new era of multi-messenger astronomy. In this paper, we present follow-up observations of the gravitational wave event GW170817 and its electromagnetic counterpart SSS17a/DLT17ck (IAU label AT2017gfo) by 14 Australian telescopes and partner observatories as part of Australian-based and Australian-led research programs. We report early-to late-time multi-wavelength observations, including optical imaging and spectroscopy, midinfrared imaging, radio imaging, and searches for fast radio bursts. Our optical spectra reveal that the transient source emission cooled from approximately 6 400 K to 2 100 K over a 7-d period and produced no significant optical emission lines. The spectral profiles, cooling rate, and photometric light curves are consistent with the expected outburst and subsequent processes of a binary neutron star merger. Star formation in the host galaxy probably ceased at least a Gyr ago, although there is evidence for a galaxy merger. Binary pulsars with short (100 Myr) decay times are therefore unlikely progenitors, but pulsars like PSR B1534+12 with its 2.7 Gyr coalescence time could produce such a merger. The displacement (∼2.2 kpc) of the binary star system from the centre of the main galaxy is not unusual for stars in the host galaxy or stars originating in the merging galaxy, and therefore any constraints on the kick velocity imparted to the progenitor are poor.

The Earth is impacted by 35-40 metre-scale objects every year. These meteoroids are the low mass end of impactors that can do damage on the ground. Despite this they are very poorly surveyed and characterised, too infrequent for ground based fireball observation efforts, and too small to be efficiently detected by NEO telescopic surveys whilst still in interplanetary space. We want to evaluate the suitability of different instruments for characterising metre-scale impactors and where they come from. We use data collected over the first 3 years of operation of the continent-scale Desert Fireball Network, and compare results with other published results as well as orbital sensors. We find that although the orbital sensors have the advantage of using the entire planet as collecting area, there are several serious problems with the accuracy of the data, notably the reported velocity vector, which is key to getting an accurate pre-impact orbit and calculating meteorite fall positions. We also outline dynamic range issues that fireball networks face when observing large meteoroid entries.

As fireball networks grow, the number of events observed becomes unfeasible to manage by manual efforts. Reducing and analysing big data requires automated data pipelines. Triangulation of a fireball trajectory can swiftly provide information on positions and, with timing information, velocities. However, extending this pipeline to determine the terminal mass estimate of a meteoroid is a complex next step. Established methods typically require assumptions to be made of the physical meteoroid characteristics (such as shape and bulk density). To determine which meteoroids may have survived entry there are empirical criteria that use a fireball's final height and velocity -low and slow final parameters are likely the best candidates. We review the more elegant approach of the dimensionless coefficient method. Two parameters, α (ballistic coefficient) and β (mass-loss), can be calculated for any event with some degree of deceleration, given only velocity and height information. α and β can be used to analytically describe a trajectory with the advantage that they are not mere fitting coefficients; they also represent the physical meteoroid properties. This approach can be applied to any fireball network as an initial identification of key events and determine on which to concentrate resources for more in depth analyses. We used a set of 278 events observed by the Desert Fireball Network to show how visualisation in an α -β diagram can quickly identify which fireballs are likely meteorite candidates.

We 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 facility optimized to recover meteorites and calculate their pre‐entry orbits. The 30 cm meteoroid entered at 15.44 km s−1, followed a moderately steep trajectory of 51° to the horizon from 81 km down to 19 km altitude, where the luminous flight ended at a speed of 3.2 km s−1. Deceleration data indicated one large fragment had made it to the ground. The four person search team recovered a 1.15 kg meteorite within 130 m of the predicted fall line, after 8 h of searching, 6 days after the fall. Dingle Dell is the fourth meteorite recovered by the DFN in Australia, but the first before any rain had contaminated the sample. By numerical integration over 1 Ma, we show that Dingle Dell was most likely ejected from the Main Belt by the 3:1 mean motion resonance with Jupiter, with only a marginal chance that it came from the ν6 resonance. This makes the connection of Dingle Dell to the Flora family (currently thought to be the origin of LL chondrites) unlikely.

We present a novel methodology for recovering meteorite falls observed and constrained by fireball networks, using drones and machine learning algorithms. This approach uses images of the local terrain for a given fall site to train an artificial neural network, designed to detect meteorite candidates. We have field tested our methodology to show a meteorite detection rate between 75% and 97%, while also providing an efficient mechanism to eliminate false positives. Our tests at a number of locations within Western Australia also showcase the ability for this training scheme to generalize a model to learn localized terrain features. Our model training approach was also able to correctly identify three meteorites in their native fall sites that were found using traditional searching techniques. Our methodology will be used to recover meteorite falls in a wide range of locations within globe-spanning fireball networks.