Results 11 to 20 of about 2,400,614 (354)

KEPLERINPUT CATALOG: PHOTOMETRIC CALIBRATION AND STELLAR CLASSIFICATION [PDF]

The Astronomical Journal, 2011
We describe the photometric calibration and stellar classification methods used to produce the Kepler Input Catalog (KIC). The KIC is a catalog containing photometric and physical data for sources in the Kepler Mission field of view; it is used by the mission to select optimal targets.
Everett, Mark, Latham, David, Brown, Timothy, Esquerdo, Gilbert   +3 more
semanticscholar   +7 more sources

Machine Learning Techniques for Stellar Light Curve Classification [PDF]

The Astronomical Journal, 2018
AbstractWe apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB ofKeplerlight curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature ...
Kevin Tat, Kevin Tat, Trisha A. Hinners, Rachel Thorp, Rachel Thorp   +4 more
semanticscholar   +10 more sources

Automated Stellar Spectral Classification and Parameterization for the Masses [PDF]

, 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,
Ted von Hippel, Carlos Allende Prieto, C. Sneden   +2 more
core   +6 more sources

Stellar classification from single-band imaging using machine learning [PDF]

, 2016
Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their diffraction pattern ...
T. Kuntzer, M. Tewes, F. Courbin
semanticscholar   +5 more sources

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, Sheelu Abraham, Aniruddha Kembhavi, Aniruddha Kembhavi, Kaushal Sharma, Ajit Kembhavi, Thirupathi Sivarani   +6 more
openaire   +4 more sources

Commission 45: Stellar Classification: (Classification Stellaire) [PDF]

Transactions of the International Astronomical Union, 2000
This report does not intend to give a complete review of all the papers that appeared since the previous General Assembly but to focus on a few subjects only.
M. Gerbaldi, Thomas Lloyd Evans, Juan Ciaria, C. J. Corbally, J. S. Drilling, Catherine D. Garmany, R. O. Gray, D. W. Kurtz, O. H. Levato, P. North, K. Zdanavičius   +10 more
openalex   +3 more sources

Classification of large-scale stellar spectra based on the non-linearly assembling learning machine [PDF]

, 2015
An important problem to be solved of traditional classification methods is they cannot deal with large-scale classification because of very high time complexity. In order to solve above problem, inspired by the thinking of collaborative management, the non-
Zhongbao Liu, Lipeng Song, Wenjuan Zhao
openalex   +2 more sources

Dusty Stellar Source Classification by Implementing Machine Learning Methods Based on Spectroscopic Observations in the Magellanic Clouds

The Astrophysical Journal
Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations using color–magnitude diagrams and infrared ...
Sepideh Ghaziasgar, Mahdi Abdollahi, Atefeh Javadi, Jacco Th. van Loon, Iain McDonald, Joana Oliveira, Amirhossein Masoudnezhad, Habib G. Khosroshahi, Bernard H. Foing, Fatemeh Fazel Hesar   +9 more
doaj   +2 more sources

A machine-vision method for automatic classification of stellar halo substructure [PDF]

Monthly notices of the Royal Astronomical Society, 2019
Tidal debris structures formed from disrupted satellites contain important clues about the assembly histories of galaxies. To date, studies of these structures have been hampered by reliance on by-eye identification and morphological classification ...
David Hendel, Kathryn V. Johnston, Rohit K. Patra, Bodhisattva Sen   +3 more
openalex   +3 more sources

Quantitative Stellar Classification with Low-Resolution Spectroscopy [PDF]

Proceedings of the International Astronomical Union, 2012
AbstractLow-resolution spectroscopy (R ≈ 1000) is used to efficiently characterize faint stars suspected to host planets. Stellar parameters, i.e. effective temperature, surface gravity, and metallicity can be assessed from these spectra by methods of quantitative classification.
M. Ammler‐von Eiff, Daniel Sebastian, E. W. Guenther   +2 more
openalex   +3 more sources