Predicting the surface age of chondritic S-type asteroids using the space weathering features in reflectance spectra: Small data machine learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985831%3A_____%2F25%3A00637542" target="_blank" >RIV/67985831:_____/25:00637542 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985815:_____/25:00637542

Výsledek na webu

<a href="https://hdl.handle.net/11104/0368564" target="_blank" >https://hdl.handle.net/11104/0368564</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1051/0004-6361/202554173" target="_blank" >10.1051/0004-6361/202554173</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting the surface age of chondritic S-type asteroids using the space weathering features in reflectance spectra: Small data machine learning

Popis výsledku v původním jazyce

The surfaces of airless planetary bodies, such as S-type asteroids, undergo space weathering (SW) due to exposure to the interplanetary environment, resulting in alterations to their reflectance spectral features (e.g., spectral slope, albedo, and absorption band characteristics). Aims. This study aims to estimate the surface age of S-, Sq-, and Q-type asteroids as a function of SW agents and dose by employing machine learning models. Methods. Two models were developed: an ensemble model (combining a CNN, gradient-boosting regressor, K-nearest neighbor, extratree regressor, and random forest regressor) and a Gaussian process (GP) model. Both models were trained on published reflectance spectra of olivine, pyroxene, their mixtures, and chondritic meteorites, using SW conditions as independent variables and surface age at 1 AU as the dependent variable. Given the limited dataset, k-fold cross-validation was employed for model training. The models were further validated by applying them to S-, Sq-, and Q-type asteroids, evaluating their ability to capture two key trends: the SW progression across chondritic S-type asteroids and the relationship between asteroid size and surface age. Results. Both models successfully identify relatively fresh surfaces in Q-type asteroids and mature surfaces in S-type asteroids, as well as younger surface ages for asteroids with diameters less than 5 km. However, the GP model exhibits higher variability in predictions for the asteroid dataset. While both models effectively capture relative surface age trends, limitations in data availability between 10(3) and 10(7) years hinder precise predictions of asteroid surface ages. Conclusions. These models have significant potential for future applications, such as determining the surface age for individual asteroids and identifying asteroid families, offering valuable tools for advancing our understanding of asteroid evolution and SW processes.

Název v anglickém jazyce

Predicting the surface age of chondritic S-type asteroids using the space weathering features in reflectance spectra: Small data machine learning

Popis výsledku anglicky

The surfaces of airless planetary bodies, such as S-type asteroids, undergo space weathering (SW) due to exposure to the interplanetary environment, resulting in alterations to their reflectance spectral features (e.g., spectral slope, albedo, and absorption band characteristics). Aims. This study aims to estimate the surface age of S-, Sq-, and Q-type asteroids as a function of SW agents and dose by employing machine learning models. Methods. Two models were developed: an ensemble model (combining a CNN, gradient-boosting regressor, K-nearest neighbor, extratree regressor, and random forest regressor) and a Gaussian process (GP) model. Both models were trained on published reflectance spectra of olivine, pyroxene, their mixtures, and chondritic meteorites, using SW conditions as independent variables and surface age at 1 AU as the dependent variable. Given the limited dataset, k-fold cross-validation was employed for model training. The models were further validated by applying them to S-, Sq-, and Q-type asteroids, evaluating their ability to capture two key trends: the SW progression across chondritic S-type asteroids and the relationship between asteroid size and surface age. Results. Both models successfully identify relatively fresh surfaces in Q-type asteroids and mature surfaces in S-type asteroids, as well as younger surface ages for asteroids with diameters less than 5 km. However, the GP model exhibits higher variability in predictions for the asteroid dataset. While both models effectively capture relative surface age trends, limitations in data availability between 10(3) and 10(7) years hinder precise predictions of asteroid surface ages. Conclusions. These models have significant potential for future applications, such as determining the surface age for individual asteroids and identifying asteroid families, offering valuable tools for advancing our understanding of asteroid evolution and SW processes.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Astronomy & Astrophysics

ISSN

0004-6361

e-ISSN

1432-0746

Svazek periodika

699

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

15

Strana od-do

A175

Kód UT WoS článku

001525746000008

EID výsledku v databázi Scopus

2-s2.0-105010630650