Results 11 to 20 of about 64,358 (76)

Deep Learning for Galaxy Mergers in the Galaxy Main Sequence [PDF]

, 2019
Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate.
Pearson, William J., Petrillo, Carlo E., Trayford, James, van der Tak, Floris F. S., Wang, Lingyu   +4 more
core   +3 more sources

The embedded clusters DBS 77, 78, 102, and 160-161 and their link with the interstellar medium [PDF]

, 2016
Aims. We report a study of the global properties of some embedded clusters placed in the fourth quadrant of the Milky Way to clarify some issues related with their location into the Galaxy and their stellar formation processes. Methods.
Alegría, S. Ramirez, Baume, G. L., Borissova, J., Chené, A. N., Corti, M. A., Kurtev, R., Panei, J. A., Suad, L. A., Testori, J. C.   +8 more
core   +3 more sources

Stellar spectrum classifier [PDF]

, 1970
Stellar classification is accomplished by correlating holograms of classified spectra with the Fourier transform of the spectral images to be ...
Reid, J. H.
core   +1 more source

Kepler Input Catalog: Photometric Calibration and Stellar Classification

, 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 ...
, Abazajian, Abazajian, Castelli, Chaplin, Christensen-Dalsgaard, Cox, David W. Latham, Fischer, Gilbert A. Esquerdo, Høg, Koch, Mark E. Everett, Metcalfe, Molenda-Zakowicz, Monet, Perryman, Skrutskie, Smith, Stoughton, Timothy M. Brown, Yi, Zacharias   +22 more
core   +1 more source

Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis [PDF]

, 1997
We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the Michigan Spectral
Bailer-Jones, Coryn A. L., Irwin, Mike, von Hippel, Ted   +2 more
core   +3 more sources

Automated Classification of Stellar Spectra using the Sloan Digital Sky Survey [PDF]

, 2019
The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS).
Brice, Michael
core   +1 more source

(Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks [PDF]

, 1999
New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly.
Beers, Timothy C., Hippel, Ted von, Lambert, David L., Prieto, Carlos Allende, Qu, Yuan, Rossi, Silvia, Sneden, Christopher, Snider, Shawn   +7 more
core   +3 more sources

Galaxy Zoo: the dependence of morphology and colour on environment [PDF]

, 2008
We analyse the relationships between galaxy morphology, colour, environment and stellar mass using data for over 100,000 objects from Galaxy Zoo, the largest sample of visually classified morphologies yet compiled.
Adelman-McCarthy, Alexander S. Szalay, Allen, Ann, Anže Slosar, Aragón-Salamanca, Baldry, Baldry, Ball, Ball, Balogh, Balogh, Bamford, Barr, Bedregal, Bedregal, Biviano, Blakeslee, Blanton, Blanton, Blanton, Blanton, Blanton, Boselli, Bower, Burstein, Butcher, Cameron, Ceccarelli, Chris J. Lintott, Christlein, Christopher J. Miller, Conselice, Conselice, Cooray, Couch, Cowie, Croton, Croton, Croton, Dan Andreescu, Daniel Thomas, Desai, Dressler, Dressler, Dressler, Dressler, Edward M. Edmondson, Faber, Fan, Font, Fukugita, Gallazzi, Gavazzi, Gisler, Glazebrook, Goto, Goto, Gunn, Gómez, Haines, Haines, Hogg, Hubble, Hubble, Huchra, Huchra, Ivan K. Baldry, James, Jan Vandenberg, Jones, Kaiser, Kate Land, Kauffmann, Kauffmann, Kauffmann, Kevin Schawinski, Koopmann, Lahav, Land, Lane, Larson, Lewis, Lin, Lintott, Lupton, M. Jordan Raddick, Maller, Mehlert, Mehri Torki, Merritt, Mihos, Miller, Mo, Mo, Moore, Nakamura, Nulsen, Padmanabhan, Park, Phil Murray, Pimbblet, Poggianti, Poggianti, Poggianti, Poggianti, Postman, Postman, Robert C. Nichol, Rojas, Rojas, Schawinski, Schawinski, Schawinski, Sheth, Simard, Skibba, Skibba, Slosar, Smail, Smith, Steven P. Bamford, Stoughton, Strauss, Torki, Treu, van den Bergh, van den Bergh, van der Wel, van der Wel, Vogt, von der Linden, Wake, Weinmann, Whitmore, Whitmore, Wolf, Wolf, Wolf, Yamauchi, Yoon, Zehavi   +141 more
core   +3 more sources

Classification of Stars from Redshifted Stellar Spectra utilizing Machine Learning [PDF]

, 2019
The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). Stellar spectra from the
Brice, Michael J.
core   +1 more source

Observational evidence for the origin of X-ray sources in globular clusters [PDF]

, 2007
Low-mass X-ray binaries, recycled pulsars, cataclysmic variables and magnetically active binaries are observed as X-ray sources in globular clusters. We discuss the classification of these systems, and find that some presumed active binaries are brighter
Cees Bassa, Dave Pooley, Frank Verbunt, Heggie, Hünsch, McCluskey, Ruggles, van der Sluys, Verbunt, Verbunt, Verbunt, Webb   +11 more
core   +2 more sources