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arXiv 2103.15586astro-ph.EPastro-ph.IMcs.NE
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M1:2与火星平均运动共振中小行星的人工神经网络分类

Artificial Neural Network classification of asteroids in the M1:2 mean-motion resonance with Mars

V. Carruba, S. Aljbaae, R. C. Domingos, W. Barletta

摘要:

人工神经网络(ANN)在过去几年中已成功地用于识别天文图像中的模式。然而,人工神经网络在小行星动力学领域的应用迄今为止还是有限的。在这项工作中,我们第一次使用人工神经网络的目的,自动识别受M1:2平均运动与火星共振的小行星轨道的行为。我们的模型在识别小行星共振参数的图像时,在准确度、精确度和召回率等标准指标方面的表现都能够达到85%以上,从而能够识别该地区所有编号小行星的轨道类型。我们还使用监督机器学习方法,通过使用遗传算法进行优化,预测了该地区所有多轨道小行星的轨道状态。我们确认,M1:2共振主要影响的Massalia,Nysa和灶神星小行星家族的轨道。

英文摘要:

Artificial neural networks (ANN) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85% levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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