J/A+A/699/A175 Asteroids predicted/corrected surface ages (Palamakumbure+, 2025)
Predicting the surface age of chondritic S-type asteroids using the space
weathering features in reflectance spectra: Small data machine learning.
Palamakumbure L., Syrjaenen S.A.I., Korda D., Kohout T., Klami A.
<Astron. Astrophys. 699, A175 (2025)>
=2025A&A...699A.175P 2025A&A...699A.175P (SIMBAD/NED BibCode)
ADC_Keywords: Solar system ; Minor planets ; Models
Keywords: methods: data analysis - methods: numerical -
techniques: spectroscopic - meteorites, meteors, meteoroids -
minor planets, asteroids: general
Abstract:
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).
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.
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.
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 103 and 107 years
hinder precise predictions of asteroid surface ages.
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.
Description:
This table presents the predicted and corrected surface ages (in
years) of the asteroids using both the Ensemble and Gaussian Process
(Gaussian (GP)) models. The columns include the asteroid number,
taxonomy based on the Bus-DeMeo classification system, solar wind
irradiation age, and micrometeorite irradiation age, both at 1 AU and
at the semi-major axis of each asteroid. near Earth Asteroids (NEAs)
are marked with '*'.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
tableb1.dat 96 218 Predicted and the corrected surface age for
these asteroids
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See also:
B/astorb : Orbits of Minor Planets (Bowell+, 2014-)
Byte-by-byte Description of file: tableb1.dat
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Bytes Format Units Label Explanations
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1- 6 I6 --- No Asteroid number
7 A1 --- n_No [*] * Near Earth Asteroids
9- 10 A2 --- Type [Q S Sq] Asteroid type
13- 16 F4.2 10+3yr T-1AU1 H+ irradiation age, surface age at 1AU,
Ensemble model
18- 21 F4.2 10+3yr e_T-1AU1 H+ irradiation age, surface age at 1AU error,
Ensemble model
24- 28 F5.2 10+3yr T-cor1 H+ irradiation age, corrected surface age
with the distance, Ensemble model
30- 33 F4.2 10+3yr e_T-cor1 H+ irradiation age, corrected surface age
with the distance error, Ensemble model
35- 38 F4.2 10+3yr T-1AU2 H+ irradiation age, surface age at 1AU,
Gaussian (GP) model
40- 44 F5.2 10+3yr e_T-1AU2 H+ irradiation age, surface age at 1AU error,
Gaussian (GP) model
46- 50 F5.2 10+3yr T-cor2 H+ irradiation age, corrected surface age
with the distance, Gaussian (GP) model
52- 56 F5.2 10+3yr e_T-cor2 H+ irradiation age, corrected surface age
with the distance error, Gaussian (GP) model
58- 61 F4.2 10+9yr T-1AU3 Laser irradiation age, surface age at 1AU,
Ensemble model
63- 66 F4.2 10+9yr e_T-1AU3 Laser irradiation age, surface age at 1AU
error, Ensemble model
68- 71 F4.2 10+9yr T-cor3 Laser irradiation age, corrected surface age
with the distance, Ensemble model
73- 76 F4.2 10+9yr e_T-cor3 Laser irradiation age, corrected surface age
with the distance error, Ensemble model
78- 81 F4.2 10+9yr T-1AU4 Laser irradiation age, surface age at 1AU,
Gaussian (GP) model
83- 86 F4.2 10+9yr e_T-1AU4 Laser irradiation age, surface age at 1AU
error, Gaussian (GP) model
88- 91 F4.2 10+9yr T-cor4 Laser irradiation age, corrected surface age
with the distance, Gaussian (GP) model
93- 96 F4.2 10+9yr e_T-cor4 Laser irradiation age, corrected surface age
with the distance error, Gaussian (GP) model
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Acknowledgements:
Lakshika Palamakumbure, lakshika.palamakumbure(at)helsinki.fi
(End) Patricia Vannier [CDS] 30-May-2025