Related papers: Meteoroid stream identification with HDBSCAN unsup…

The mass ranges of meteors, imaged by electro-optical (EO) cameras and backscatter radar receivers, for the most part do not overlap. Typical EO systems detect meteoroid masses down to 10$^{-5}$ kg or roughly magnitude +2 meteors when using…

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…

We use the Slovak and Czech video meteor observations, as well as video meteoroid orbits collected in the CAMS, SonotaCo, EDMOND and DMS catalogues, for an analysis of the distribution of meteoroid orbits within the stream of the Geminids…

Solar active regions (ARs) are the primary source of solar eruptions and space weather. Accurate detection and tracking of ARs is crucial for understanding their evolution and predicting solar activities. In the previous work, based on the…

Context. Dynamically linking a meteor shower with its parent body is challenging, and chaos in the dynamics of meteoroid streams may be one of the reasons. For a robust identification of parent bodies, it is therefore necessary to quantify…

We statistically evaluate and compare four orbital similarity criteria within five-dimensional parameter space ($D_{SH}$, $D_D$, $D_H$, and $\varrho_2$) to study dynamical associations using the already classified meteors (manually by a…

In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid…

HDBSCAN is a density-based clustering algorithm that constructs a cluster hierarchy tree and then uses a specific stability measure to extract flat clusters from the tree. We show how the application of an additional threshold value can…

Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-based clustering algorithms are particularly well-suited for…

Up to 150000 asteroids will be visible in the images of the ESA Euclid space telescope, and the instruments of Euclid offer multiband visual to near-infrared photometry and slitless spectra of these objects. Most asteroids will appear as…

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes…

We apply the clustering algorithm HDBSCAN on the Gaia early third data release astrometry combined with the Gaia second data release radial velocity measurements of almost 5.5 million stars to identify the local stellar kinematic…

Atom probe tomography is commonly used to study solute clustering and precipitation in materials. However, standard techniques, such as the density based spatial clustering applications with noise (DBSCAN) perform poorly with respect to…

Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) finds meaningful patterns in spatial data by considering density and spatial proximity. As the clustering algorithm is inherently designed for static…

Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based…

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…

Over the past decade there has been a large increase in the number of automated camera networks that monitor the sky for fireballs. One of the goals of these networks is to provide the necessary information for linking meteorites to their…

This paper presents a comprehensive comparative analysis of prominent clustering algorithms K-means, DBSCAN, and Spectral Clustering on high-dimensional datasets. We introduce a novel evaluation framework that assesses clustering…

We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: theoretical arguments and empirical evidence show that…

Landmark recognition and matching is a critical step in many Image Navigation and Registration (INR) models for geostationary satellite services, as well as to maintain the geometric quality assessment (GQA) in the instrument data…