Photometric redshift estimation via deep learning
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[1] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[2] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[3] W. M. Wood-Vasey,et al. The Sloan Digital Sky Survey quasar catalog: ninth data release , 2012, 1210.5166.
[4] D. Thompson,et al. PHOTOMETRIC REDSHIFT AND CLASSIFICATION FOR THE XMM–COSMOS SOURCES , 2008, 0809.2098.
[5] T. Gneiting,et al. The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification , 2006 .
[6] L. Cambrésy,et al. Hierarchical progressive surveys - Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes , 2015, 1505.02291.
[7] Anton H. Westveld,et al. Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation , 2005 .
[8] F. Gieseke,et al. Finding new high-redshift quasars by asking the neighbours , 2012, 1210.7071.
[9] C. Bishop. Mixture density networks , 1994 .
[10] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[11] Alberto Fernandez-Soto,et al. On the Compared Accuracy and Reliability of Spectroscopic and Photometric Redshift Measurements , 2000, astro-ph/0007447.
[12] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[13] H. Hersbach. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .
[14] R. J. Brunner,et al. TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests , 2013, 1303.7269.
[15] W. M. Wood-Vasey,et al. THE BARYON OSCILLATION SPECTROSCOPIC SURVEY OF SDSS-III , 2012, 1208.0022.
[16] Raffaele D'Abrusco,et al. Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation , 2011, 1107.3160.
[17] Sarah Bridle,et al. Cosmology with photometric redshift surveys , 2004 .
[18] Ben Hoyle,et al. Measuring photometric redshifts using galaxy images and Deep Neural Networks , 2015, Astron. Comput..
[19] Alexander S. Szalay,et al. RANDOM FORESTS FOR PHOTOMETRIC REDSHIFTS , 2010 .
[20] M. Brescia,et al. PHOTOMETRIC REDSHIFTS FOR QUASARS IN MULTI-BAND SURVEYS , 2013, 1305.5641.
[21] M. Brescia,et al. A catalogue of photometric redshifts for the SDSS-DR9 galaxies , 2014, 1407.2527.
[22] Canada.,et al. Data Mining and Machine Learning in Astronomy , 2009, 0906.2173.
[23] A. Szalay,et al. THE SLOAN DIGITAL SKY SURVEY QUASAR CATALOG. V. SEVENTH DATA RELEASE , 2010, 1004.1167.
[24] N. Benı́tez. Bayesian Photometric Redshift Estimation , 1998, astro-ph/9811189.
[25] Manda Banerji,et al. A comparison of six photometric redshift methods applied to 1.5 million luminous red galaxies , 2008, 0812.3831.
[26] Iftach Sadeh,et al. ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning , 2015, 1507.00490.
[27] Alexander S. Szalay,et al. Photometric redshifts for the SDSS Data Release 12 , 2016, 1603.09708.
[28] Ofer Lahav,et al. ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks , 2004 .
[29] R. Laureijs,et al. Euclid: ESA's mission to map the geometry of the dark universe , 2012, Other Conferences.
[30] W. M. Wood-Vasey,et al. THE NINTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY: FIRST SPECTROSCOPIC DATA FROM THE SDSS-III BARYON OSCILLATION SPECTROSCOPIC SURVEY , 2012, 1207.7137.
[31] Massimo Brescia,et al. Machine-learning-based photometric redshifts for galaxies of the ESO Kilo-Degree Survey data release 2 , 2015 .