@Article{CarrubaAljbLucc:2019:MaIdAs,
author = "Carruba, V. and Aljbaae, Safwan and Lucchini, A.",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista
(UNESP)}",
title = "Machine-learning identification of asteroid groups",
journal = "Monthly Notices of the Royal Astronomical Society",
year = "2019",
volume = "488",
number = "1",
pages = "1377--1386",
month = "Sept.",
keywords = "methods: data analysis, celestial mechanics, minor planets,
asteroids: general.",
abstract = "Asteroid families are groups of asteroids that share a common
origin. They can be the outcome of a collision or be the result of
the rotational failure of a parent body or its satellites.
Collisional asteroid families have been identified for several
decades using hierarchical clustering methods (HCMs) in proper
elements domains. In this method, the distance of an asteroid from
a reference body is computed, and, if it is less than a critical
value, the asteroid is added to the family list. The process is
then repeated with the new object as a reference, until no new
family members are found. Recently, new machine-learning
clustering algorithms have been introduced for the purpose of
cluster classification. Here, we apply supervised-learning
hierarchical clustering algorithms for the purpose of asteroid
families identification. The accuracy, precision, and recall
values of results obtained with the new method, when compared with
classical HCM, show that this approach is able to found family
members with an accuracy above 89.5 per cent, and that all
asteroid previously identified as family members by traditional
methods are consistently retrieved. Values of the areas under the
curve coefficients below Receiver Operating Characteristic curves
are also optimal, with values consistently above 85 per cent.
Overall, we identify 6 new families and 13 new clumps in regions
where the method can be applied that appear to be consistent and
homogeneous in terms of physical and taxonomic properties.
Machine-learning clustering algorithms can, therefore, be very
efficient and fast tools for the problem of asteroid family
identification.",
doi = "10.1093/mnras/stz1795",
url = "http://dx.doi.org/10.1093/mnras/stz1795",
issn = "0035-8711 and 1365-2966",
language = "en",
urlaccessdate = "29 mar. 2024"
}