Meteor shower detection with density‐based clustering

Abstract

We present a new method to detect meteor showers using the density-based spatial clustering of applications with noise algorithm (DBSCAN;Ester et al. 1996). The DBSCAN algorithm is a modern cluster detection algorithm that is well suited to the problem of extracting meteor showers from all-sky camera data because of its ability to efficiently extract clusters of different shapes and sizes from large data sets. We apply this shower detection algorithm on a data set that contains 25,885 meteor trajectories and orbits obtained from the NASA All-Sky Fireball Network and the Southern Ontario Meteor Network (SOMN). Using a distance metric based on solar longitude, geocentric velocity, and Sun-centered ecliptic radiant, we find 25 strong cluster detections and six weak detections in the data, all of which are good matches to known showers. We include measurement errors in our analysis to quantify the reliability of cluster occurrence and the probability that each meteor belongs to a given cluster. We validate our method through false-positive/negative analysis and with a comparison to an established shower detection algorithm.

Modification of the Shower Database of the IAU Meteor Data Center

“…Moreover, the evaluation of a shower's statistical significance in its local sporadic background is one of the new criteria for achieving established status (see Section 4.2). There are various procedures that reflect the strength of a shower compared to its local sporadic background; for example the break-point method developed by [17] (see also the detailed description of the method by [18]), the methods introduced by [19] and [20], or the methods suitable in the case of radar data such as the 3D wavelet transform by (author?) [21] or its recent improvement by (author?)…”

“…4.2). There are various procedures that reflect the strength of a shower compared to its local sporadic background; for example the break-point method developed by Neslušan et al (1995, see also the detailed description of the method by Vaubaillon et al 2019), the methods introduced by Moorhead (2016) and Sugar et al (2017), or the methods suitable in the case of radar data such as the 3D wavelet transform by Brown et al (2008) or its recent improvement by Kipreos et al (2022). Another approach is to estimate the probability of a random coincidence of two orbits, which helps to set a threshold value of the D-discriminant for a specific sample of orbits, and, thus, discriminate between the related orbits and those which are similar by chance (Jopek & Bronikowska 2017 Hajduková & Neslušan 2020).…”

Meteor shower radiant dispersions in Global Meteor Network data

“…While they are considered meteor showers, the Taurids appear to lie somewhere between meteor shower and sporadic source in their 2 computed using the haversine formula characteristics. Furthermore, because the Nothern and Southern Taurids are two branches of the same complex, have similar orbits, and are active at the same time and with similar radiants, most shower identification techniques struggle to separate the two branches (see, for example, Sugar et al 2017).…”

Modeling of the meteoroid stream of comet C/1975 T2 and λ-Ursae Majorids

“…Our paper is similar to recent similar studies of the relationship between various meteoroid streams and their parent bodies, i.e., cometary (Hajdukova et al 2015;Ishiguro et al 2015;Kornoš et al 2015;Rudawska et al 2016;Abedin et al 2015Abedin et al , 2017Abedin et al , 2018Babadzhanov et al 2017;Jenniskens et al 2017;Šegon et al 2017) and lately also asteroidal (Babadzhanov et al 2015a,b;Jopek 2015;Jopek & Williams 2015;Rudawska & Vaubaillon 2015;Olech et al 2016;Wiegert et al 2017;Dumitru et al 2018;Sergienko et al 2018a,b;Ye 2018;Guennoun et al 2019;Ryabova et al 2019). Some authors have attempted to work out or improve a method of prediction of particular shower, often on the basis of known parent body (Koten & Vaubaillon 2015;Ryabova 2016;Sugar et al 2017;Vaubaillon 2017;Ryabova & Rendtel 2018a,b). All this effort is highly desirable in the current era when a number of new showers as well as a number of new members of known showers are reported every year (e.g., Jones 2018;Jenniskens et al 2018;Koukal 2018;Molau et al 2018a,b,c;Shiba et al 2018;Toth et al 2018;Vida et al 2018a,b;Wisniewski et al 2018, if we consider only the last year).…”