@Article{CarrubaAljDomHuaBar:2022:MaLeAp,
author = "Carruba, Valerio and Aljbaae, Safwan and Domingos, R. C. and
Huaman, M. and Barletta, W.",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista
(UNESP)} and {Universidad tecnol{\'o}gica del Per{\'u} (UTP)}
and {Universidade Estadual Paulista (UNESP)}",
title = "Machine learning applied to asteroid dynamics",
journal = "Celestial Mechanics and Dynamical Astronomy",
year = "2022",
volume = "134",
number = "4",
pages = "e36",
month = "Aug.",
keywords = "Asteroid belt, Celestial mechanics, Chaotic motions, Statistical
methods.",
abstract = "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 learn a general rule
that maps inputs to outputs, and unsupervised learning, where no
label is provided to the learning algorithm, leaving it alone to
find structures. Deep learning is a branch of machine learning
based on numerous layers of artificial neural networks, which are
computing systems inspired by the biological neural networks that
constitute animal brains. In asteroid dynamics, machine learning
methods have been recently used to identify members of asteroid
families, small bodies images in astronomical fields, and to
identify resonant arguments images of asteroids in three-body
resonances, among other applications. Here, we will conduct a full
review of available literature in the field and classify it in
terms of metrics recently used by other authors to assess the
state of the art of applications of machine learning in other
astronomical subfields. For comparison, applications of machine
learning to Solar System bodies, a larger area that includes
imaging and spectrophotometry of small bodies, have already
reached a state classified as progressing. Research communities
and methodologies are more established, and the use of ML led to
the discovery of new celestial objects or features, or new
insights in the area. ML applied to asteroid dynamics, however, is
still in the emerging phase, with smaller groups, methodologies
still not well-established, and fewer papers producing discoveries
or insights. Large observational surveys, like those conducted at
the Zwicky Transient Facility or at the Vera C. Rubin Observatory,
will produce in the next years very substantial datasets of
orbital and physical properties for asteroids. Applications of ML
for clustering, image identification, and anomaly detection, among
others, are currently being developed and are expected of being of
great help in the next few years.",
doi = "10.1007/s10569-022-10088-2",
url = "http://dx.doi.org/10.1007/s10569-022-10088-2",
issn = "0923-2958",
language = "en",
targetfile = "s10569-022-10088-2.pdf",
urlaccessdate = "28 mar. 2024"
}