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(433) Eros and (25143) Itokawa surface properties from reflectance spectra

Korda, D. and Kohout, T. and Flanderová, K. and Vincent, J. B. and Penttilä, A. (2023) (433) Eros and (25143) Itokawa surface properties from reflectance spectra. Astronomy & Astrophysics. EDP Sciences. doi: 10.1051/0004-6361/202346290. ISSN 0004-6361.

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Official URL: https://dx.doi.org/10.1051/0004-6361/202346290


Context. Our knowledge of near-Earth asteroid (NEA) composition is important for planetary research, planetary defence, and future in-space resource utilisation. Upcoming space missions, for example, Hera, M-ARGO, or missions to the asteroid (99942) Apophis, will provide us with surface-resolved NEA reflectance spectra. Neural networks are useful tools for analysing reflectance spectra and determining material composition with high precision and low processing time. Aims. We applied neural-network models on disk-resolved spectra of the Eros and Itokawa asteroids observed by the NEAR Shoemaker and Hayabusa spacecraft. With this approach, the mineral variations or intensity of space weathering can be mapped. Methods. We built and tested two types of convolutional neural networks (CNNs). The first one was trained using asteroid reflectance spectra with known taxonomy classes. The other one used silicate reflectance spectra with assigned mineral abundances and compositions. Results. The reliability of the classification model depends on the resolution of reflectance spectra. Typical F1 score and Cohen’s κC values decrease from about 0.90 for high-resolution spectra to about 0.70 for low-resolution spectra. The predicted silicate composition does not strongly depend on spectrum resolution and coverage of the 2μm band of pyroxene. The typical root mean square error is between 6 and 10 percentage points. For the Eros and Itokawa asteroids, the predicted taxonomy classes favour the S-type and the predicted surface compositions are homogeneous and correspond to the composition of L/LL and LL ordinary chondrites, respectively. On the Itokawa surface, the model identified fresh spots that were connected with craters or coarse-grain areas. Conclusions. The neural network models trained with measured spectra of asteroids and silicate samples are suitable for deriving surface silicate mineralogy with a reasonable level of accuracy. The predicted surface mineralogy is comparable to the mineralogy of returned samples measured in the laboratory. Moreover, the taxonomical predictions can point out locations of fresher areas.

Item URL in elib:https://elib.dlr.de/195260/
Document Type:Article
Additional Information:Bisher nur online erschienen.
Title:(433) Eros and (25143) Itokawa surface properties from reflectance spectra
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Vincent, J. B.UNSPECIFIEDhttps://orcid.org/0000-0001-6575-3079137604450
Date:May 2023
Journal or Publication Title:Astronomy & Astrophysics
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:EDP Sciences
Keywords:Asteroids, Spectroscopy, Machine Learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Exploration
DLR - Research area:Raumfahrt
DLR - Program:R EW - Space Exploration
DLR - Research theme (Project):R - Exploration of the Solar System
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Geodesy
Deposited By: Vincent, Jean-Baptiste
Deposited On:27 Jun 2023 10:46
Last Modified:27 Jun 2023 10:46

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