Application of convolutional neural networks for stellar spectral classification [PDF]
Monthly Notices of the Royal Astronomical Society, 2019 ABSTRACTDue 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 Teff, $\rm {\log g}$, and ...
Kaustubh Vaghmare +6 more
arxiv +5 more sources
Automated Stellar Spectral Classification and Parameterization for the Masses [PDF]
arXiv, 2002 Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been developed for and applied to large surveys. While most ongoing large spectroscopic surveys target extragalactic objects,
von Hippel, Ted +2 more
arxiv +5 more sources
On the Use of Logistic Regression for stellar classification. An application to colour-colour diagrams [PDF]
Experimental astronomy (Print), 2018 We are totally immersed in the Big Data era and reliable algorithms and methods for data classification are instrumental for astronomical research. Random Forest and Support Vector Machines algorithms have become popular over the last few years and they are widely used for different stellar classification problems.
Leire Beitia-Antero +2 more
arxiv +5 more sources
Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration [PDF]
arXiv, 2023 In this study, we introduce an innovative Quantum-enhanced Support Vector Machine (QSVM) approach for stellar classification, leveraging the power of quantum computing and GPU acceleration. Our QSVM algorithm significantly surpasses traditional methods such as K-Nearest Neighbors (KNN) and Logistic Regression (LR), particularly in handling complex ...
Kuan-Cheng Chen +4 more
arxiv +2 more sources
Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network
Advances in Astronomy, 2022 Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by
Zhuang Zhao, Jiyu Wei, Bin Jiang
doaj +2 more sources
The Carnegie-Irvine Galaxy Survey. X. Bulges in Stellar Mass–based Scaling Relations [PDF]
The Astrophysical Journal Supplement Series, 2022 We measure optical colors for the bulges of 312 disk galaxies from the Carnegie-Irvine Galaxy Survey and convert their previously available R -band structural parameters to stellar-mass parameters.
Hua Gao, Luis C. Ho, Zhao-Yu Li
doaj +2 more sources
Solar stellar and substellar environment. II. Classification and determination of main characteristics [PDF]
Вісник Астрономічної школи, 2012 The problem of classification of celestial bodies of the Galaxy which takes into account the astrophysical and cosmogonic criteria is discussed. The main ideas and arguments of the stellar, substellar and planetary minimum mass calculation are considered.
V. A. Zakhozhay, M. O. Babenko
doaj +2 more sources
Stellar Classification with Vision Transformer and SDSS Photometric Images
UniverseWith the development of large-scale sky surveys, an increasing number of stellar photometric images have been obtained. However, most stars lack spectroscopic data, which hinders stellar classification.
Yi Yang, Xin Li
doaj +2 more sources
Stellar Spectra Classification and Feature evaluation Based on Random Forest [PDF]
RAA, 19(8):111(7pp), 2019, 2019 With the availability of multi-object spectrometers and the designing \& running of some large scale sky surveys, we are obtaining massive spectra. Therefore, it becomes more and more important to deal with the massive spectral data efficiently and accurately.
Xiangru Li, Yang-Tao Lin, Kai-bin Qiu
arxiv +3 more sources