Expert systems and artificial neural networks applied to stellar optical spectroscopy: a comparative analysis

Rodrı́guez, Alejandra; Dafonte, Carlos; Arcay, B.; Manteiga, M.; Carricajo, Iciar

doi:10.1109/ijcnn.2004.1381079

Cited by 1 publication

(2 citation statements)

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“…e automatic stellar spectral classification of big astronomical databases is a challenge that has been addressed by computational intelligence techniques; among them the Artificial Neural Networks (ANNs) have proven their effectiveness and accuracy [4][5][6][7][8][9][10]. Results found in the studies mentioned previously showed that, with the Artificial Neural Network approach, it is possible to classify spectra with S : N as low as 20 with errors in the classification process lower than 2 spectral subtypes; therefore, with the advent of massive astronomical databases, the automatic methods are clearly more necessary to extract and analyze the spectral information in a fast and accurate way, even when noise is a strong characteristic in the data.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs also have a wider number of applications in astrophysics; some examples are the automatic determination of star's physical parameters [11,12], distinguishing stars from galaxies [13,14], and galaxy morphologic classification [15]. Some of the most powerful advantages of ANNs are their capacity to handle large volumes of information, resistance to noise in the data as well as to the lack of data, and the capacity to reach an accuracy on the classification process similar to that of an expert [9]. Combined with the use of Artificial Neural Networks and other classifiers, dimensionality reduction methods as Principal Component Analysis (PCA), Isomap, and index measurement have been applied, in order to reduce the size of the input vector and extract the main features for classification [8,10,16]; in these works, the authors reported that dimensionality reduction methods are efficient to extract and retain the most useful information in the spectra and thus can be used in the classification process.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks for the Automatic Spectral Classification

Vilavicencio-Arcadia

Navarro

Corral

et al. 2020

Mathematical Problems in Engineering

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Classification in astrophysics is a fundamental process, especially when it is necessary to understand several aspects of the evolution and distribution of the objects. Over an astronomical image, we need to discern between stars and galaxies and to determine the morphological type for each galaxy. The spectral classification of stars provides important information about stellar physical parameters like temperature and allows us to determine their distance; with this information, it is possible to evaluate other parameters like their physical size and the real 3D distribution of each type of objects. In this work, we present the application of two Artificial Intelligence (AI) techniques for the automatic spectral classification of stellar spectra obtained from the first data release of LAMOST and also to the more recent release (DR5). Two types of Artificial Neural Networks were selected: a feedforward neural network trained according to the Levenberg–Marquardt Optimization Algorithm (LMA) and a Generalized Regression Neural Network (GRNN). During the study, we used four datasets: the first was obtained from the LAMOST first data release and consisted of 50731 spectra with signal-to-noise ratio above 20, the second dataset was obtained from the Indo-US spectral database (1273 spectra), the third one (the STELIB spectral database) was used as an independent test dataset, and the fourth dataset was obtained from LAMOST DR5 and consisted of 17990 stellar spectra with signal-to-noise ratio above 20 also. The results in the first part of the work, when the autoconsistency of the DR1 data was probed, showed some problems in the spectral classification available in LAMOST DR1. In order to accomplish a better classification, we made a two-step process: first the LAMOST and STELIB datasets were classified by the two IA techniques trained with the entire Indo-US dataset. The resulted classification allows us to discriminate at least three groups: the first group contained O and B type stars, whereas the second contained A, F, and G type stars, and finally, the third group contained K and M type stars. The second step consisted of a refinement of the classification, but this time for every group, the most relevant indices were selected. We compared the accuracy reached by the two techniques when they are trained and tested using LAMOST spectra and their published classification and the resultant classifications obtained with the ANNs trained with the Indo-US dataset and applied over the STELIB and LAMOST spectra. Finally, in the first part, we compared the LAMOST DR1 classification with the classification obtained by the application of the NNs GRNNs and LMA trained with the Indo-US dataset. In the second part of the paper, we analyze a set of 17990 stellar spectra from LAMOST DR5 and the very significant improvement in the spectral classification available in DR5 database was verified. For this, we trained ANNs using the k-fold cross-validation technique with k = 5.

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