Results 21 to 30 of about 2,400,614 (354)

Stellar classification from simulated DIVA spectra. I. Solar metallicity stars

Open Astronomy, 1999
. The orbiting telescope with the Double Interferometer for Visual Astrometry (DIVA) will obtain, apart from astrometry, low-resolution spectra (A/AA ~ 20) covering the optical range (350 to 1000 nm) for over 10 million stars.
Elsner B., Bastian U., Liubertas R., Scholz R.   +3 more
doaj   +2 more sources

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks [PDF]

, 2005
An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification.
Ling Bai, Ping Guo, Zhanyi Hu
openalex   +2 more sources

A Comparative Study of KBS, ANN and Statistical Clustering Techniques for Unattended Stellar Classification [PDF]

Iberoamerican Congress on Pattern Recognition, 2005
Carlos Dafonte, Alejandra Rodrı́guez, Bernardino Arcay, Iciar Carricajo, M. Manteiga   +4 more
openalex   +2 more sources

Automatic Extraction and Classification of Low-Dispersion Objective-Prism Stellar Spectra [PDF]

Open Astronomy, 2000
Bratsolis E., Maragoudaki F., Bellas-Velidis I., Dapergolas A., Kontizas E., Kontizas M.   +5 more
doaj   +4 more sources

The Classification of Stellar Spectra

The Astrophysical Journal, 1935
Neil Russell, Cecilia Helena Payne Gaposchkin, Donald H. Menzel   +2 more
openalex   +3 more sources

Towards a Multi-Dimensional Stellar Classification System

Open Astronomy, 1997
Stock J., Agostinho R., Rose J . A., Marin Z., Molina R.   +4 more
doaj   +3 more sources

Gaia Data Release 3. Apsis. III. Non-stellar content and source classification [PDF]

Astronomy & Astrophysics, 2022
Context. As part of the third Gaia data release, we present the contributions of the non-stellar and classification modules from the eighth coordination unit (CU8) of the Data Processing and Analysis Consortium, which is responsible for the determination
L. Delchambre, C. Bailer-Jones, I. Bellas-Velidis, R. Drimmel, D. Garabato, R. Carballo, D. Hatzidimitriou, D. Marshall, R. Andrae, C. Dafonte, E. Livanou, M. Fouesneau, E. Licata, H. Lindstrøm, M. Manteiga, C. Robin, A. Silvelo, A. Aramburu, M. A. Álvarez, J. Bakker, A. Bijaoui, N. Brouillet, E. Brugaletta, A. Burlacu, L. Casamiquela, L. Chaoul, A. Chiavassa, G. Contursi, W. J. Cooper, O. Creevey, A. Dapergolas, P. Laverny, C. Demouchy, T. Dharmawardena, B. Edvardsson, Y. Frémat, P. Garcı́a-Lario, M. García-Torres, A. Gavel, A. Gomez, I. González-Santamaría, U. Heiter, A. Piccolo, M. Kontizas, G. Kordopatis, A. Korn, A. Lanzafame, Y. Lebreton, A. Lobel, A. Lorca, A. Romeo, F. Marocco, N. Mary, C. Nicolas, C. Ordenovic, F. Pailler, P. Palicio, L. Pallas-Quintela, C. Panem, B. Pichon, E. Poggio, A. Recio-Blanco, F. Riclet, J. Rybizki, R. Santoveña, L. Sarro, M. Schultheis, M. Ségol, I. Ślęzak, R. Smart, R. Sordo, C. Soubiran, M. Suveges, F. Thévenin, G. T. Elipe, A. Ulla, E. Utrilla, A. Vallenari, E. W. Dillen, H. Zhao, J. Zorec   +80 more
semanticscholar   +1 more source

Mira variables in the Milky Way’s nuclear stellar disc: discovery and classification [PDF]

Monthly notices of the Royal Astronomical Society, 2022
The properties of the Milky Way’s nuclear stellar disc give crucial information on the epoch of bar formation. Mira variables are promising bright candidates to study the nuclear stellar disc, and through their period–age relation dissect its star ...
J. Sanders, N. Matsunaga, D. Kawata, L. Smith, D. Minniti, P. Lucas   +5 more
semanticscholar   +1 more source

TESS Data for Asteroseismology (T’DA) Stellar Variability Classification Pipeline: Setup and Application to the Kepler Q9 Data [PDF]

Astronomical Journal, 2021
The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from ∼27 days to about 1 yr of continuous observations.
J. Audenaert, J. Kuszlewicz, R. Handberg, A. Tkachenko, David J Armstrong, M. Hon, R. Kgoadi, M. Lund, K. Bell, L. Bugnet, D. Bowman, C. Johnston, R. A. Garc'ia, D. Stello, L. Moln'ar, E. Plachy, D. Buzasi, C. Aerts, the T'DA collaboration   +18 more
semanticscholar   +1 more source

Stellar Classification based on Various Star Characteristics using Machine Learning Algorithms

Journal of student-scientists' research, 2023
The task of stellar classification can be tedious and lengthy when done manually. One can expedite stellar classification by creating an artificial intelligence model to automate the process.
Jesus Tamez Villarreal, Sophia Barton
semanticscholar   +1 more source