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Scaling uncertainties on asteroid characteristics to prepare datasets for machine learning
Advances in Space Research ( IF 2.6 ) Pub Date : 2021-06-19 , DOI: 10.1016/j.asr.2021.06.007
Marco Agnan , Jordan Vannitsen

Context

Physical and dynamical characterisations of asteroids are used in different fields, such as Solar System formation modelling, Planetary Defence and Resources Prospecting. The vast majority of asteroids are not known in detail - have at best their orbit well defined - and the knowledge on the composition or internal structure is derived by models of reflectivity curves, with limited certainty. Machine learning methods have begun to be used on asteroid datasets, but the major uncertainties about their characteristics are slowing down the applicability.

Aims

This paper reviews some stakes and challenges of asteroid exploration, and why the introduction of common characterisation factors would be beneficial for the asteroid science community, especially with the application of machine learning methodologies. A preliminary scale to quantify the characterisation of asteroids is proposed, and finally discusses its interests and limitations for machine learning applications.

Method

The investigated characteristics of asteroids are: size/shape, orbital dynamics, mass/density, spin, internal structure and composition. This paper reviews the current methods used to determine these parameters, and provides a preliminary scale based on the certainty associated with the different measurements.

Results

Characterisation factors are useful to build datasets that will be used in machine learning algorithms applied to asteroid science. The ratio of currently known asteroids in each defined bin of characterisation factor is estimated. Moreover, a total characterisation factor that yield a preliminary quantification of our knowledge about a specific asteroid (i.e. the sum of all the certainties about its different characteristics) is defined. Finally, characterisation factors for specific applications can be introduced using an adapted weighting system.

Next steps

This preliminary work provides a baseline for scaling uncertainties of asteroids properties. The next step is to create viable datasets using application specific characterisation factors that would allow the use of advanced machine learning algorithms already available.



中文翻译:

缩放小行星特征的不确定性,为机器学习准备数据集

语境

小行星的物理和动力学特征用于不同领域,例如太阳系形成建模、行星防御和资源勘探。绝大多数小行星的细节尚不清楚——它们的轨道至多是明确定义的——关于成分或内部结构的知识是通过反射率曲线模型得出的,但确定性有限。机器学习方法已经开始用于小行星数据集,但其特性的主要不确定性正在减缓适用性。

宗旨

本文回顾了小行星探索的一些利害关系和挑战,以及为什么引入通用特征因素对小行星科学界有益,尤其是在机器学习方法的应用中。提出了量化小行星特征的初步尺度,最后讨论了它对机器学习应用的兴趣和局限性。

方法

小行星的研究特征是:大小/形状、轨道动力学、质量/密度、自旋、内部结构和组成。本文回顾了当前用于确定这些参数的方法,并根据与不同测量相关的确定性提供了一个初步的量表。

结果

特征因子对于构建将用于应用于小行星科学的机器学习算法的数据集非常有用。估计每个定义的特征因子箱中当前已知小行星的比率。此外,还定义了一个总特征因子,它可以初步量化我们对特定小行星的了解(即关于其不同特征的所有确定性的总和)。最后,可以使用经过调整的加权系统引入特定应用的特征因子。

后续步骤

这项初步工作为缩放小行星特性的不确定性提供了基线。下一步是使用特定于应用程序的特征因素创建可行的数据集,从而允许使用现有的高级机器学习算法。

更新日期:2021-08-24
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