Related papers: Generating realistic synthetic meteoroid orbits

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for…

Cooperation and data sharing among national networks and International Meteor Organization Video Meteor Database (IMO VMDB) resulted in European viDeo MeteOr Network Database (EDMOND). The current version of the database (EDMOND 5.0)…

The generation of synthetic data is an essential tool to study complex systems, allowing for example to test models of these in precisely controlled settings, or to parametrize simulation models when data is missing. This paper focuses on…

Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and…

As observational datasets become larger and more complex, so too are the questions being asked of these data. Data simulations, i.e., synthetic data with properties (pixelization, noise, PSF, artifacts, etc.) akin to real data, are…

An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks. In this regard, a common shortcoming of current benchmark datasets is their…

This is an overview of recent research on meteors and the parent bodies from which they are produced. While many meteor showers result from material ejected by comets, two out of the three strongest annual showers (the Geminids and the…

Meteors are important phenomenon reflecting many properties of interplanetary dust particles. The study of their origin, mass distribution, and orbit evolution all require large data volume, which can only be obtained using large meteor…

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate…

We present a novel orbit parameterization in spherical coordinates. This parameterization enables the mixing of varying and invariant orbital parameters, and clarifies the physics of the orbit. It also simplifies the process of placing…

As of today, there is no official definition of a meteor cluster. It is usually identified as a large number of meteors sharing a similar radiant and velocity, all occurring within a few seconds. Only eight clusters have been reported so…

Statistical analysis of samples of the orbits of celestial bodies is complicated by the fact that the Keplerian orbit is a multidimensional object, the coordinate representation of which nonlinearly depends on the choice of orbital…

The computation of the Minimum Orbital Intersection Distance (MOID) is an old, but increasingly relevant problem. Fast and precise methods for MOID computation are needed to select potentially hazardous asteroids from a large catalogue. The…

Synthetic data generation is a promising technique to facilitate the use of sensitive data while mitigating the risk of privacy breaches. However, for synthetic data to be useful in downstream analysis tasks, it needs to be of sufficient…

Kernel density estimators with circular data have been studied extensively for decades, as they allow flexible estimations even when the shape of the underlying density is complex. Many recent studies have examined bias correction methods;…

Orbital dissimilarity, or D, criteria are often used to select members of a meteor shower from a set of meteor observations. These criteria provide a quantitative description of the degree to which two orbits differ; if the degree of…

The Geminids meteoroid stream produces one of the most intense meteor showers at Earth. It is an unusual stream in that its parent body is understood to be an asteroid, (3200) Phaethon, unlike most streams which are formed via ongoing…

Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techniques do not give the data controller control over what…

The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets can be a powerful tool to provide a better understanding of a…

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their…