Starmind: A Fuzzy Logic Knowledge-Based System for the Automated Classification of Stars in the Mk System

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

doi:10.1088/0004-6256/137/2/3245

Cited by 30 publications

(27 citation statements)

References 27 publications

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“…They found that only ANNs could equal the human expert precision. Manteiga et al (2009) used ANNs and fuzzy reasoning to classify stellar spectra. The precision they reach in A&A 538, A76 (2012) spectral classification is similar to that of human experts in more than 80% of the sample.…”

Section: Introductionmentioning

confidence: 99%

Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks

Navarro

Corradi

Mampaso

2012

A&A

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Context. As part of a project aimed at deriving extinction-distances for thirty-five planetary nebulae, spectra of a few thousand stars were analyzed to determine their spectral type and luminosity class. Aims. We present here the automatic spectral classification process used to classify stellar spectra. This system can be used to classify any other stellar spectra with similar or higher signal-to-noise ratios. Methods. Spectral classification was performed using a system of artificial neural networks that were trained with a set of line-strength indices selected among the spectral lines most sensitive to temperature and the best luminosity tracers. The training and validation processes of the neural networks are discussed and the results of additional validation probes, designed to ensure the accuracy of the spectral classification, are presented. Results. Our system permits the classification of stellar spectra of signal-to-noise ratio (S /N) significantly lower than it is generally considered to be needed. For S /N ≥ 20, a precision generally better than two spectral subtypes is obtained. At S /N < 20, classification is still possible but has a lower precision. Its potential to identify peculiar sources, such as emission-line stars, is also recognized.

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Section: Introductionmentioning

confidence: 99%

Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks

Navarro

Corradi

Mampaso

2012

A&A

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“…In the probabilistic mode of classification, they obtained an error of 2.09 subclasses. Manteiga et al (2009) proposed a classifier, STARMIND, which tries to mimic the human reasoning for stellar classification by using a knowledge base comprising of spectral features computed from template spectra. The authors deployed an expert system (ES) which uses this knowledge base and applies fuzzy logic for classifying spectra with an overall accuracy of 82.7% at the main-class level.…”

Section: Introductionmentioning

confidence: 99%

Application of convolutional neural networks for stellar spectral classification

Sharma

Kembhavi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters T eff , log g, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using Convolutional Neural Networks. Traditional machine learning (ML) methods with "shallow" architecture (usually up to 2 hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalisation. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like Artificial Neural Networks (ANN), Random Forest (RF), and Convolutional Neural Network (CNN) to classify stellar spectra using the Jacoby Atlas, ELODIE and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using convolutional neural networks, we are able to lower the error up to 1.23 spectral sub-classes as compared to that of 2 sub-classes achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS database with SNR>20.

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“…are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/612/A98 Numerous works have been done that explore the performance of the automatic MK classification of spectra (see e.g. Bailer-Jones et al 1998;Singh et al 1998;Bailer-Jones 2002;Rodríguez et al 2004;Giridhar et al 2006;Manteiga et al 2009;Navarro et al 2012). The main approach followed in these works was to apply supervised learning training using labelled data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in APOGEE

Garcia‐Dias

Prieto

Alméida

et al. 2018

A&A

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Context. The volume of data generated by astronomical surveys is growing rapidly. Traditional analysis techniques in spectroscopy either demand intensive human interaction or are computationally expensive. In this scenario, machine learning, and unsupervised clustering algorithms in particular, offer interesting alternatives. The Apache Point Observatory Galactic Evolution Experiment (APOGEE) offers a vast data set of near-infrared stellar spectra, which is perfect for testing such alternatives. Aims. Our research applies an unsupervised classification scheme based on K-means to the massive APOGEE data set. We explore whether the data are amenable to classification into discrete classes. Methods. We apply the K-means algorithm to 153 847 high resolution spectra (R ≈ 22 500). We discuss the main virtues and weaknesses of the algorithm, as well as our choice of parameters. Results. We show that a classification based on normalised spectra captures the variations in stellar atmospheric parameters, chemical abundances, and rotational velocity, among other factors. The algorithm is able to separate the bulge and halo populations, and distinguish dwarfs, sub-giants, RC, and RGB stars. However, a discrete classification in flux space does not result in a neat organisation in the parameters' space. Furthermore, the lack of obvious groups in flux space causes the results to be fairly sensitive to the initialisation, and disrupts the efficiency of commonly-used methods to select the optimal number of clusters. Our classification is publicly available, including extensive online material associated with the APOGEE Data Release 12 (DR12). Conclusions. Our description of the APOGEE database can help greatly with the identification of specific types of targets for various applications. We find a lack of obvious groups in flux space, and identify limitations of the K-means algorithm in dealing with this kind of data.

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Starmind: A Fuzzy Logic Knowledge-Based System for the Automated Classification of Stars in the Mk System

Cited by 30 publications

References 27 publications

Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks

Automatic spectral classification of stellar spectra with low signal-to-noise ratio using artificial neural networks

Application of convolutional neural networks for stellar spectral classification

Machine learning in APOGEE

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