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Space Weathering Age of Itokawa and Eros by Machine Learning




TekijätPalamakumbure, Lakshika; Korda, David; Kohout, Tomáš

KustantajaInstitute of Physics Publishing

Julkaisuvuosi2026

Lehti: The Planetary Science Journal

Artikkelin numero65

Vuosikerta7

Numero3

eISSN2632-3338

DOIhttps://doi.org/10.3847/PSJ/ae4749

Julkaisun avoimuus kirjaamishetkelläAvoimesti saatavilla

Julkaisukanavan avoimuus Kokonaan avoin julkaisukanava

Verkko-osoitehttps://doi.org/10.3847/psj/ae4749

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/523364488

Rinnakkaistallenteen lisenssiCC BY

Rinnakkaistallennetun julkaisun versioKustantajan versio


Tiivistelmä

Near-Earth asteroids such as Itokawa and Eros provide valuable insights into the collisional and surface evolution of the inner solar system. As S-type asteroids, their surfaces are altered by space weathering (SW) from solar wind and micrometeorite impacts. Because SW progresses over time, it serves as a proxy for estimating surface exposure ages, revealing resurfacing and geologic histories. This study estimates the SW ages at the present conditions of Itokawa and Eros using a machine learning model developed by L. Palamakumbure et al. The ensemble model was trained on laboratory reflectance spectra of irradiated silicate samples (olivine, pyroxene, mixtures, and chondrites) to capture spectral changes due to SW. Asteroid spectra were obtained from the Near Infrared Spectrometer (Hayabusa) dataset for Itokawa and the Near-Infrared Spectrometer (NEAR Shoemaker) dataset for Eros via NASA Planetary Data System. SW ages for Itokawa range from 1.9 kyr to 2.5 Gyr, reflecting alteration from both solar wind and micrometeorite impacts. Eros, in contrast, shows older SW ages (0.4 to 2 Gyr), dominated by micrometeorite effects. These results align with previous studies and Hayabusa sample analyses, confirming Itokawa’s young surfaces are influenced by solar wind, with older, less disturbed regions such as Arcoona. Eros displays more uniformly mature surfaces, with localized relatively young areas linked to cratering or regolith movement. The contrasting SW ages highlight how asteroid size, regolith behavior, and orbital characteristics influence SW processes. Overall, the study demonstrates the potential of machine learning for reconstructing surface exposure histories on airless bodies.


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This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




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This work was supported by the Doctoral program of the University of Helsinki and the institutional support RVO 67985831 of the Institute of Geology of the Czech Academy of Science. The open access publication cost was funded by Helsinki University Library.


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