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Register a new userA Dynamic Trajectory Fit to Multi-Sensor Fireball Observations
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Trent Jansen-Sturgeon Mr
Publication date
2019
fields
Physics
and research's language is
English
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Meteorites with known orbital origins are key to our understanding of Solar System formation and the source of life on Earth. However, these pristine samples of space material are incredibly rare. Less than 40 of the 60,000 meteorites held in collections around the world have known dynamical origins. Fireball networks have been developed globally in a unified effort to increase this number by using multiple observatories to record, triangulate, and dynamically analyse ablating meteoroids as they enter our atmosphere. The accuracy of the chosen meteoroid triangulation method directly influences the accuracy of the determined orbit and the likelihood of possible meteorite recovery. There are three leading techniques for meteoroid triangulation discussed in the literature: the Method of Planes, the Straight Line Least Squares method, and the Multi-Parameter Fit method. Here we describe an alternative method to meteoroid triangulation, called the Dynamic Trajectory Fit. This approach uses the meteoroids 3D dynamic equations of motion to fit a realistic trajectory directly to multi-sensor line-of-sight observations. This method has the ability to resolve fragmentation events, fit systematic observatory timing offsets, and determine mass estimates of the meteoroid along its observable trajectory. Through a comprehensive Monte-Carlo analysis of over 100,000 trajectory simulations, we find this new method to more accurately estimate meteoroid trajectories of slow entry events ($<$25,km/s) and events observed from low convergence angles ($<$10$^{circ}$) compared to existing meteoroid triangulation techniques. Additionally, we triangulate an observed fireball event with visible fragmentation using the various triangulation methods to show that the proposed Dynamic Trajectory Fit implementing fragmentation to best match the captured multi-sensor line-of-sight data.
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The worlds meteorite collections contain a very rich picture of what the early Solar System would have been made of, however the lack of spatial context with respect to their parent population for these samples is an issue. The asteroid population is equally as rich in surface mineralogies, and mapping these two populations (meteorites and asteroids) together is a major challenge for planetary science. Directly probing asteroids achieves this at a high cost. Observing meteorite falls and calculating their pre-atmospheric orbit on the other hand, is a cheaper way to approach the problem. The Global Fireball Observatory (GFO) collaboration was established in 2017 and brings together multiple institutions (from Australia, USA, Canada, Morocco, Saudi Arabia, the UK, and Argentina) to maximise the area for fireball observation time and therefore meteorite recoveries. The members have a choice to operate independently, but they can also choose to work in a fully collaborative manner with other GFO partners. This efficient approach leverages the experience gained from the Desert Fireball Network (DFN) pathfinder project in Australia. The state-of-the art technology (DFN camera systems and data reduction) and experience of the support teams is shared between all partners, freeing up time for science investigations and meteorite searching. With all networks combined together, the GFO collaboration already covers 0.6% of the Earths surface for meteorite recovery as of mid-2019, and aims to reach 2% in the early 2020s. We estimate that after 5 years of operation, the GFO will have observed a fireball from virtually every meteorite type. This combined effort will bring new, fresh, extra-terrestrial material to the labs, yielding new insights about the formation of the Solar System.
Despite ablation and drag processes associated with atmospheric entry of meteoroids were a subject of intensive study over the last century, little attention was devoted to interpret the observed fireball terminal height. This is a key parameter because it not only depends on the initial mass, but also on the bulk physical properties of the meteoroids and hence of their ability to ablate in the atmosphere. In this work we have developed a new approach that is tested using the fireball terminal heights observed by the Meteorite Observation and Recovery Project operated in Canada between 1970-1985 (hereafter referred as MORP). We then compare them to the calculation made. Our results clearly show that the new methodology is able to forecast the degree of deepening of meteoroids in the Earths atmosphere. Then, this approach has important applications in predicting the impact hazard from cm- to meter-sized bodies that are represented, in part, in the MORP bolide list.
Objects gravitationally captured by the Earth-Moon system are commonly called temporarily captured orbiters (TCOs), natural Earth satellites, or minimoons. TCOs are a crucially important subpopulation of near-Earth objects (NEOs) to understand because they are the easiest targets for future sample-return, redirection, or asteroid mining missions. Only one TCO has ever been observed telescopically, 2006 RH 120, and it orbited Earth for about 11 months. Additionally, only one TCO fireball has ever been observed prior to this study. We present our observations of an extremely slow fireball (codename DN160822_03) with an initial velocity of around 11.0 km s-1 that was detected by six of the high-resolution digital fireball observatories located in the South Australian region of the Desert Fireball Network. Due to the inherent dynamics of the system, the probability of the meteoroid being temporarily captured before impact is extremely sensitive to its initial velocity. We examine the sensitivity of the fireballs orbital history to the chosen triangulation method. We use the numerical integrator REBOUND to assess particle histories and assess the statistical origin of DN160822_03. From our integrations we have found that the most probable capture time, velocity, semimajor axis, NEO group, and capture mechanism vary annually for this event. Most particles show that there is an increased capture probability during Earths aphelion and perihelion. In the future, events like these may be detected ahead of time using telescopes like the Large Synoptic Survey Telescope, and the pre-atmospheric trajectory can be verified.
To accurately predict future positions of different agents in traffic scenarios is crucial for safely deploying intelligent autonomous systems in the real-world environment. However, it remains a challenge due to the behavior of a target agent being affected by other agents dynamically and there being more than one socially possible paths the agent could take. In this paper, we propose a novel framework, named Dynamic Context Encoder Network (DCENet). In our framework, first, the spatial context between agents is explored by using self-attention architectures. Then, the two-stream encoders are trained to learn temporal context between steps by taking the respective observed trajectories and the extracted dynamic spatial context as input. The spatial-temporal context is encoded into a latent space using a Conditional Variational Auto-Encoder (CVAE) module. Finally, a set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context by sampling from the latent space, repeatedly. DCENet is evaluated on one of the most popular challenging benchmarks for trajectory forecasting Trajnet and reports a new state-of-the-art performance. It also demonstrates superior performance evaluated on the benchmark inD for mixed traffic at intersections. A series of ablation studies is conducted to validate the effectiveness of each proposed module. Our code is available at https://github.com/wtliao/DCENet.
Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning. In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. Since the underlying interactions may evolve even with abrupt changes, and different modalities of evolution may lead to different outcomes, we address the necessity of dynamic relational reasoning and adaptively evolving the interaction graphs. We also introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance. The proposed framework is evaluated on both synthetic physics simulations and multiple real-world benchmark datasets in various areas. The experimental results illustrate that our approach achieves state-of-the-art performance in terms of prediction accuracy.
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