Related papers: A Machine Learning Approach to Meteor Classificati…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to…
Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such…
Meteoroid bulk density is a critical value required for assessing impact risks to spacecraft, informing shielding and mission design. Direct bulk density measurements for sub-millimeter to millimeter-sized meteoroids are difficult, often…
The classification of the minor bodies of the Solar System based on observables has been continuously developed and iterated over the past 40 years. While prior iterations followed either the availability of large observational campaigns or…
Hazardous asteroid has been one of the concerns for humankind as fallen asteroid on earth could cost a huge impact on the society.Monitoring these objects could help predict future impact events, but such efforts are hindered by the large…
Meteoroids impacting the Earth on a daily basis are fragments of asteroids and comets. By studying fireballs produced during their disintegration in the atmosphere, we can gain information about their source regions and the properties of…
The possibility of determining cosmological parameters on the basis of a wide set of observational data including the Abell-ACO cluster power spectrum and mass function, peculiar velocities of galaxies, the distribution of Ly-$\alpha$…
This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness in the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of the old binary cluster…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
The possibility of determining cosmological parameters on the basis of a wide set of observational data including the Abell-ACO cluster power spectrum and mass function, peculiar velocities of galaxies, the distribution of Ly-$\alpha$…
It has recently been shown by Egal et al. (2017) that some types of existing meteor in-atmosphere trajectory estimation methods may be less accurate than others, particularly when applied to high precision optical measurements. The…
Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual…
In meteor science, the identification of meteor showers is a crucial and complex problem. The most common method is to perform a systematic search of a database of observed orbits using an orbit dissimilarity criterion (D-criterion) and an…
To date only very few meteor clusters have been instrumentally recorded. This means that every new detection is an important contribution to the understanding of these phenomena, which are thought to be evidence of the meteoroid…
We report the first-time recovery of a fresh meteorite fall using a drone and a machine learning algorithm. A fireball on the 1st April 2021 was observed over Western Australia by the Desert Fireball Network, for which a fall area was…
Accurate estimation of meteoroid bulk density is crucial for assessing spacecraft impact hazards from sub-millimeter to millimeter-sized meteoroids. Previous studies often used manual tuning or optimization methods to fit ablation and…
Solar wind classification is conducive to understand the physical processes ongoing at the Sun and solar wind evolution in the interplanetary space, and furthermore, it is helpful for early warning of space weather events. With rapid…
A cluster analysis was applied to the combined meteoroid orbit database derived from low-light level video observations by the SonotaCo consortium in Japan (64,650 meteors observed between 2007 and 2009) and by the Cameras for All-sky…
Meteoroids pose one of the largest risks to spacecraft outside of low Earth orbit. In order to correctly predict the rate at which meteoroids impact and damage spacecraft, environment models must describe the mass, directionality, velocity,…