Taxonomic analysis of asteroids with artificial neural networks
Nanping Luo Xiaobin Wang Shenghong Gu Antti Penttilä Karri Muinonen Yisi Liu
We study the surface composition of asteroids with visible and/or infrared
spectroscopy. For example, asteroid taxonomy is based on the spectral features
or multiple color indices in visible and near-infrared wavelengths. The
composition of asteroids gives key information to understand their origin and
evolution. However, we lack compositional information for faint asteroids due
to limits of ground-based observational instruments. In the near future, the
Chinese Space Survey telescope (CSST) will provide multiple colors and
spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and
23 mag, respectively. For the aim of analysis of the CSST spectroscopic data,
we applied an algorithm using artificial neural networks (ANNs) to establish a
preliminary classification model for asteroid taxonomy according to the design
of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel
taxonomy system, our ANN classification tool composed of 5 individual ANNs is
constructed, and the accuracy of this classification system is higher than 92
%. As the first application of our ANN tool, 64 spectra of 42 asteroids
obtained in 2006 and 2007 by us with the 2.16-m telescope in the Xinglong
station (Observatory Code 327) of National Astronomical Observatory of China
are analyzed. The predicted labels of these spectra using our ANN tool are
found to be reasonable when compared to their known taxonomic labels.
Considering the accuracy and stability, our ANN tool can be applied to analyse
the CSST asteroid spectra in the future.