^1,2Masahiko Sato,^3,4Kosuke Kurosawa,²Sunao Hasegawa,⁵Futoshi Takahashi
Journal of Geophysical Research (Planets)(in Press) Open Access Link to Article [https://doi.org/10.1029/2023JE007864]
¹Department of Earth and Planetary Science, The University of Tokyo, Tokyo, Japan
²Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, Japan
³Department of Human Environmental Science, Kobe University, Kobe, Japan
⁴Planetary Exploration Research Center, Chiba Institute of Technology, Narashino, Japan
⁵Department of Earth and Planetary Sciences, Kyushu University, Fukuoka, Japan
Published by arrangement with John Wiley & Sons

Knowledge of the shock remanent magnetization (SRM) property is crucial for interpreting the spatial change in a magnetic anomaly observed over an impact crater. This study conducted two series of impact-induced SRM acquisition experiments by varying the applied field intensity (0–400 μT) and impact conditions. Systematic remanence measurements of cube-shaped subsamples cut from shocked basalt containing single-domain titanomagnetite were conducted to investigate the effects of changes in pressure and temperature on the SRM acquisition. The peak pressure and temperature distributions in the shocked samples were estimated using shock-physics modeling. SRM intensity was proportional to the applied field intensity of up to 400 μT. SRM intensity data for peak pressure and temperature of up to 8.0 GPa and 530 K, respectively, clearly show that it increases with increasing pressure and decreases with increasing temperature. The SRM has unblocking temperature components up to a Curie temperature of 510 K, and it easily demagnetizes with alternating field demagnetization. The observed SRM properties can be explained by the pressure-induced microcoercivity reduction and temperature-induced modification of the blocking curve. Although the remanence acquisition efficiency of the SRM is significantly lower than that of the thermoremanent magnetization (TRM), the magnetic anomaly originating from the SRM distribution in a broader region may show a contribution comparable to that of the impact-induced TRM distribution in a narrow region.

¹Evgeny Smirnov
Icarus (in Press) Link to Article [https://doi.org/10.1016/j.icarus.2024.116058]
¹Evgeny SmirnovBelgrade Astronomical Observatory, Volgina 7, 11060, Belgrade, Serbia
Copyright Elsevier

This study explores how well various machine learning classifiers can identify mean-motion resonances in the main belt using supervised learning. The most popular classifiers are assessed: k-Nearest Neighbours, Decision Tree, Gradient Boosting, AdaBoost, Random Forest, and Naïve Bayes. In contrast to previous studies that often relied on default ML configurations, this research conducts a detailed investigation, fine-tuning, and testing of each classifier across various parameters. The results show that simpler models, especially k-Nearest Neighbours and Decision Tree, perform better than more complex ones, particularly in terms of
scores. The paper provides guides on selecting features, parameters, and training set sizes for optimal classifier performance and outlines a method for developing effective machine-learning models for asteroid classification.