Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms

• URL: http://arxiv.org/abs/2204.05075v1
• Date: Mon, 11 Apr 2022 13:24:54 GMT
• Title: Zero-phase angle asteroid taxonomy classification using unsupervised machine learning algorithms
• Authors: M. Colazo, A. Alvarez-Candal, and R. Duffard
• Abstract summary: We produce a new taxonomic classification of asteroids based on their magnitudes unaffected by the change in phase angle. We used a unsupervised machine learning algorithm known as fuzzy C-means to perform the classification. We identify 15 new V-type asteroid candidates outside the Vesta family region.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We are in an era of large catalogs and, thus, statistical analysis tools for large data sets, such as machine learning, play a fundamental role. One example of such a survey is the Sloan Moving Object Catalog (MOC), which lists the astrometric and photometric information of all moving objects captured by the Sloan field of view. One great advantage of this telescope is represented by its set of five filters, allowing for taxonomic analysis of asteroids by studying their colors. However, until now, the color variation produced by the change of phase angle of the object has not been taken into account. In this paper, we address this issue by using absolute magnitudes for classification. We aim to produce a new taxonomic classification of asteroids based on their magnitudes that is unaffected by variations caused by the change in phase angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz, computed from the Sloan Moving Objects Catalog using the HG12 system. We calculated the absolute colors with them. To perform the taxonomic classification, we applied a unsupervised machine learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with {data sets where the different groups are not completely separated and there are regions of overlap between them. We have chosen to work with the four main taxonomic complexes, C, S, X, and V, as they comprise most of the known spectral characteristics. We classified a total of 6329 asteroids with more than 60% probability of belonging to the assigned taxonomic class, with 162 of these objects having been characterized by an ambiguous classification in the past. By analyzing the sample obtained in the plane Semimajor axis versus inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.

Related papers

• Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources [44.99833362998488]
  We develop an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses.
  arXiv  Detail & Related papers  (2024-01-22T18:42:31Z)
• Taxonomic analysis of asteroids with artificial neural networks [7.274273862904249]
  In the near future, the Chinese Space Survey telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and 23 mag. We apply an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy. Using the SMASS II spectra and the Bus-Binzel taxonomy system, our ANN classification tool composed of 5 individual ANNs is constructed.
  arXiv  Detail & Related papers  (2023-11-18T03:27:26Z)
• Spiral-Elliptical automated galaxy morphology classification from telescope images [0.40792653193642503]
  We develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics.
  arXiv  Detail & Related papers  (2023-10-10T22:36:52Z)
• Cosmology from Galaxy Redshift Surveys with PointNet [65.89809800010927]
  In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. We employ a textitPointNet-like neural network to regress the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of $mathcalO(104) - mathcalO(105)$ galaxies at a time, which improves upon earlier work for this application by roughly two orders of magnitude.
  arXiv  Detail & Related papers  (2022-11-22T15:35:05Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images [0.6445605125467573]
  In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. The achieved test accuracy is 84.73 per cent which happens to be promising after considering such minute details in classes.
  arXiv  Detail & Related papers  (2022-11-01T11:43:21Z)
• Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks [70.80563014913676]
  We show that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty, of such parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values.
  arXiv  Detail & Related papers  (2022-07-07T17:55:26Z)
• Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques [1.3764085113103222]
  Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Here, we train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters. We are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes)
  arXiv  Detail & Related papers  (2022-01-05T13:37:55Z)
• Morphological Classification of Galaxies in S-PLUS using an Ensemble of Convolutional Networks [0.0]
  We combine accurate visual classifications of the Galaxy Zoo project with emph Deep Learning methods. A neural network model was created through an Ensemble of four other convolutional models, allowing a greater accuracy in the classification than what would be obtained with any one individual.
  arXiv  Detail & Related papers  (2021-07-05T21:51:19Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• Learnable Subspace Clustering [76.2352740039615]
  We develop a learnable subspace clustering paradigm to efficiently solve the large-scale subspace clustering problem. The key idea is to learn a parametric function to partition the high-dimensional subspaces into their underlying low-dimensional subspaces. To the best of our knowledge, this paper is the first work to efficiently cluster millions of data points among the subspace clustering methods.
  arXiv  Detail & Related papers  (2020-04-09T12:53:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.