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arXiv 2210.01006astro-ph.EPastro-ph.IMcs.LG
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从反射光谱确定小行星矿物成分的神经网络

Neural network for determining an asteroid mineral composition from reflectance spectra

University of Helsinki(赫尔辛基大学);Czech Academy of Sciences(捷克科学院)

David Korda, Antti Penttilä, Arto Klami, Tomáš Kohout

摘要:

小行星的化学和矿物成分反映了我们太阳系的形成和历史。这些知识对于行星防御和空间资源利用也很重要。我们的目标是开发一种快速和强大的基于神经网络的方法,用于从可见光和近红外光谱中获得硅酸盐材料的矿物模态和化学组成。该方法应能够处理原始光谱,无需进行大量预处理。我们设计了一个具有两个隐藏层的卷积神经网络来分析光谱,并使用标记的反射光谱对其进行训练。对于训练,我们使用了一个数据集,该数据集由存储在RELAB和C-Tape数据库中的真实硅酸盐样品的反射光谱组成,即橄榄石,斜方辉石,单斜辉石,它们的混合物,以及富含橄榄石-辉石的陨石。我们在两个数据集上使用了该模型。首先,我们在测试数据集上评估了模型的可靠性,其中我们将模型分类与已知的成分参考值进行了比较。个体分类结果大多在正确值周围的10个间隔点范围内。其次,我们分类的反射光谱的S-复杂的(Q-型和V-型,也包括A-型)小行星与已知的Bus-DeMeo分类类。预测的S型和Q型小行星的矿物化学成分与普通陨石的化学成分一致。V型和A型小行星的模态丰度分别显示出斜方辉石和橄榄石的主要贡献。此外,我们的预测的矿物模态组成的S-型和Q-型小行星显示出明显的橄榄石耗尽与衰减的诊断与空间风化。这一趋势与以前的结果相一致,即辉石对空间风化的反应比橄榄石慢。

英文摘要:

Chemical and mineral compositions of asteroids reflect the formation and history of our Solar System. This knowledge is also important for planetary defence and in-space resource utilisation. We aim to develop a fast and robust neural-network-based method for deriving the mineral modal and chemical compositions of silicate materials from their visible and near-infrared spectra. The method should be able to process raw spectra without significant pre-processing. We designed a convolutional neural network with two hidden layers for the analysis of the spectra, and trained it using labelled reflectance spectra. For the training, we used a dataset that consisted of reflectance spectra of real silicate samples stored in the RELAB and C-Tape databases, namely olivine, orthopyroxene, clinopyroxene, their mixtures, and olivine-pyroxene-rich meteorites. We used the model on two datasets. First, we evaluated the model reliability on a test dataset where we compared the model classification with known compositional reference values. The individual classification results are mostly within 10 percentage-point intervals around the correct values. Second, we classified the reflectance spectra of S-complex (Q-type and V-type, also including A-type) asteroids with known Bus-DeMeo taxonomy classes. The predicted mineral chemical composition of S-type and Q-type asteroids agree with the chemical composition of ordinary chondrites. The modal abundances of V-type and A-type asteroids show a dominant contribution of orthopyroxene and olivine, respectively. Additionally, our predictions of the mineral modal composition of S-type and Q-type asteroids show an apparent depletion of olivine related to the attenuation of its diagnostic absorptions with space weathering. This trend is consistent with previous results of the slower pyroxene response to space weathering relative to olivine.

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