Investigating Machine Learning as a Basis for Asteroid Taxnomies in the 3-Micron Spectral Region
Authors: Richardson Matthew , Rivkin Andrew , Sickafoose Amanda 
Published: October 2020

Abstract:As part of a larger study to elucidate the presence of hydrated minerals on asteroid surfaces, we are developing a robust taxonomic classification system using spectroscopic observations in the vicinity of 3 &#956;m. We have constructed a Python algorithm to identify band centers and band depths near 3 &#181;m for a set of normalized, thermally-corrected asteroid spectra for use to serve as inputs to Python&#8217;s Scikit-Learn library of Machine Learning (ML) algorithms. We anticipate a thorough investigation of both Principal Component Analysis and ML (supervised, unsupervised, and Artificial Neural Network) techniques to assess which technique is likely to be better suited for classifying the 3-&#181;m data. At this writing, we have run tests using Python&#8217;s Agglomerative clustering ML algorithm to examine possible clustering scenarios. These initial steps have given us some familiarity with the mechanics of using ML on the 3-&#181;m dataset as well as serving to identify some possible pitfalls or cul-de-sacs. Presented here are the preliminary results we have obtained.Introduction:Although various techniques have been used, asteroid classification has typically been done via Principal Component Analysis (PCA: [1,2]). PCA is a statistical technique that reduces the dimensionality of a dataset by identifying the most important parameters within a dataset based on their variance. Parameters that exhibit the greatest amount of variance are considered to be of greater importance while parameters with the least amount of variance are considered to be of lower importance. While the PCA technique produces better visualizations of the data by reducing the dimensionality of a dataset, the PCA technique comes with some drawbacks. Disadvantages such as its dependence on scale and information loss due to the orthogonal property of PCA can cause interpretation of PCA results to prove to be a more critical and time-consuming process. Therefore, exploring other means of classification may prove to be worthwhile.Machine Learning (ML) algorithms have had a significant impact on the way in which data is analyzed and interpreted, and have already proven to be a powerfully reliable resource in the field of planetary science. Accordingly, the application of ML to an asteroid taxonomy has the potential to be more efficient, objective, and easy-to-implement than PCA. ML algorithms can be supervised, in which the program &#8220;learns&#8221; from training data and is able to classify new inputs, or unsupervised, in which the program analyzes the dataset to determine patterns such as clusters. [3] used an Artificial Neural Network (ANN, a subset of ML) to classify asteroids, work followed up by [4]. Recent explorations of supervised ML for asteroid taxonomy are promising, and have applied training sets from existing databases to new visible and/or NIR photometric data (e.g. [5,6,7]).We seek to explore the benefits of ML algorithms, as well as compare and contrast to the PCA technique, in the production of an asteroid taxonomy. Our initial exploration has utilized a set of normalized, thermally-corrected asteroid spectra in the vicinity of 3 &#181;m. We have identified band centers and band depths and served this parameter space as inputs to Python&#8217;s Agglomerative clustering ML algorithm.Methodology:Thermal corrections of the asteroid spectra were performed via a forward model that uses a modified version of the Standard Thermal Model (STM: [8]). The forward model treats the beaming parameter as a free parameter adjusting its value for each iteration of the STM until it converges onto a value that yields expected long-wavelength continuum behavior. Spectra were then normalized to unity at a wavelength of 2.3 &#181;m, followed by identification of band centers and band depths near 3 &#181;m using both polynomial and Gaussian fits. In addition, band depths were measured at wavelengths of 2.9 &#181;m and 3.2 &#181;m to gather more information on asteroid band shapes. Lastly, the aforementioned calculated spectral features were input into Python&#8217;s Agglomerative clustering algorithm to determine which asteroid spectra shared similar features.Summary:As part of a larger investigation to better understand hydrated mineralogies as they apply to asteroids, we have begun work towards developing a quantitative taxonomic framework derived from asteroid spectra in the wavelength range from 2.0-4.0 &#181;m. Our exploration thus far of Python&#8217;s Agglomerative clustering algorithm has proven to be fruitful. Minor changes to the parameterization of this algorithm can yield very different results, which naturally can lead to different interpretations. The Agglomerative clustering algorithm is one of many the powerful ML algorithms we will explore against the PCA technique, all of which we will be discussing in our presentation.References:[1] Bus, S. J. and Binzel, R. P. (2002). Phase II of the Small Main-Belt Asteroid Spectroscopic Survey: A Feature-Based Taxonomy. Icarus, 158:146-177.[2] DeMeo, F. E., Binzel, R. P., Slivan, S. M., and Bus, S. J. (2009). An extension of the Bus asteroid taxonomy into the near-infrared. Icarus, 202:160-180.[3] Howell, E. S., Merenyi, E., and Lebofsky, L. A. (1994). Classification of asteroid spectra using a neural network. 99:10847-10865.[4] Merenyi, E., Howell E. S., Rivkin A. S., and Lebofsky L. A. (1997). Prediction of water in asteroids from spectral data shortward of 3 &#181;m. Icarus, 129:421-439.[5] Moment, M., Trilling, D. 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