Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation
暂无分享,去创建一个
Raffaele D'Abrusco | Omar Laurino | Giuseppe Longo | Giuseppe Riccio | G. Longo | G. Riccio | O. Laurino | R. D’abrusco
[1] K. Abazajian,et al. THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY , 2008, 0812.0649.
[2] Ofer Lahav,et al. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks , 2004 .
[3] Robert J. Brunner,et al. Robust Machine Learning Applied to Astronomical Data Sets. III. Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX , 2008, 0804.3413.
[4] D. Wake,et al. MegaZ-LRG:a photometric redshift catalogue of one million SDSS luminous red galaxies , 2006, astro-ph/0607630.
[5] N. Davey,et al. Photometric redshift estimation using Gaussian processes , 2009 .
[6] Alexander S. Szalay,et al. Photometric redshifts from reconstructed quasar templates , 2001 .
[7] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[8] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[9] D. Schlegel,et al. Maps of Dust Infrared Emission for Use in Estimation of Reddening and Cosmic Microwave Background Radiation Foregrounds , 1998 .
[10] Kirk D. Borne,et al. Astroinformatics: A 21st Century Approach to Astronomy , 2009, ArXiv.
[11] Department of Physics,et al. Luminosity Functions from Photometric Redshifts I: Techniques , 1996 .
[12] Ashok N. Srivastava,et al. Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..
[13] I. Smail,et al. The All-Wavelength Extended Groth Strip International Survey (AEGIS) Data Sets , 2006, astro-ph/0607355.
[14] D. Schlegel,et al. Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds , 1997, astro-ph/9710327.
[15] D. Thompson,et al. PHOTOMETRIC REDSHIFT AND CLASSIFICATION FOR THE XMM–COSMOS SOURCES , 2008, 0809.2098.
[16] Y. Wadadekar. Estimating Photometric Redshifts Using Support Vector Machines , 2004, astro-ph/0412005.
[17] Michigan.,et al. Estimating photometric redshifts with artificial neural networks , 2002, astro-ph/0203250.
[18] A. Amara,et al. Photometric redshifts for weak lensing tomography from space: the role of optical and near infrared photometry , 2007, 0705.1437.
[19] Alexander S. Szalay,et al. RANDOM FORESTS FOR PHOTOMETRIC REDSHIFTS , 2010 .
[20] Canada.,et al. Data Mining and Machine Learning in Astronomy , 2009, 0906.2173.
[21] M. Paolillo,et al. The properties of the heterogeneous Shakhbazyan groups of galaxies in the SDSS , 2009, 0903.1093.
[22] A. Szalay,et al. GALEX–SDSS CATALOGS FOR STATISTICAL STUDIES , 2009, 0904.1392.
[23] A. Szalay,et al. THE SLOAN DIGITAL SKY SURVEY QUASAR CATALOG. V. SEVENTH DATA RELEASE , 2010, 1004.1167.
[24] D. Gerdes,et al. PHAT: PHoto-z Accuracy Testing , 2010, 1008.0658.
[25] Carlos E. C. J. Gabriel,et al. Astronomical Data Analysis Software and Systems Xv , 2022 .
[26] J. C. Dunn,et al. A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .
[27] G. Longo,et al. Mining the SDSS Archive. I. Photometric Redshifts in the Nearby Universe , 2007 .
[28] Kirk D. Borne. Astroinformatics: data-oriented astronomy research and education , 2010, Earth Sci. Informatics.
[29] A. Szalay,et al. Slicing Through Multicolor Space: Galaxy Redshifts from Broadband Photometry , 1995, astro-ph/9508100.
[30] Enn Saar,et al. Recovering the real-space correlation function from photometric redshift surveys , 2008, 0812.4226.
[31] Astronomy,et al. Photometric Redshift Estimation Using Spectral Connectivity Analysis , 2009, 0906.0995.
[32] H. Yee,et al. FINDING GALAXY GROUPS IN PHOTOMETRIC-REDSHIFT SPACE: THE PROBABILITY FRIENDS-OF-FRIENDS ALGORITHM , 2008, 0801.2410.
[33] M. Way,et al. Novel Methods for Predicting Photometric Redshifts from Broadband Photometry Using Virtual Sensors , 2006 .
[34] Michael I. Jordan,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.
[35] R. Quadri,et al. QUANTIFYING PHOTOMETRIC REDSHIFT ERRORS IN THE ABSENCE OF SPECTROSCOPIC REDSHIFTS , 2009, 0910.2704.
[36] N. A. Walton,et al. Quasar candidates selection in the Virtual Observatory era , 2008, 0805.0156.
[37] Oxford,et al. The 2dF QSO Redshift Survey – XII. The spectroscopic catalogue and luminosity function , 2004, astro-ph/0403040.
[38] Alexander G. Gray,et al. Efficient photometric selection of quasars from the sloan digital sky survey: 100,000 z < 3 quasars from data release one , 2004 .
[39] A. Fontana,et al. Photometric redshifts with the Multilayer Perceptron Neural Network: Application to the HDF-S and SDSS , 2003, astro-ph/0312064.
[40] Patrick Petitjean,et al. Artificial neural networks for quasar selection and photometric redshift determination , 2010 .
[41] John E. Davis,et al. Sloan Digital Sky Survey: Early Data Release , 2002 .
[42] R. Nichol,et al. The Application of Photometric Redshifts to the SDSS Early Data Release , 2002, astro-ph/0211080.