Multiband Galaxy Morphologies for CLASH: A Convolutional Neural Network Transferred from CANDELS
暂无分享,去创建一个
Pavlos Protopapas | Marc Huertas-Company | Guillermo Cabrera-Vives | P. Protopapas | M. Huertas-Company | G. Cabrera-Vives | M. Pérez-Carrasco | M. P'erez-Carrasco | Monserrat Martinez-Mar'in | P. Cerulo | R. Demarco | Julio Godoy | Ricardo Demarco | Manuel P'erez-Carrasco | Monserrat Martinez-Mar'in | Pierluigi Cerulo | Julio Godoy | M. Martínez-Marin | J. Godoy
[1] Ivan Laptev,et al. Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[2] B. Garilli,et al. MASS AND ENVIRONMENT AS DRIVERS OF GALAXY EVOLUTION IN SDSS AND zCOSMOS AND THE ORIGIN OF THE SCHECHTER FUNCTION , 2010, 1003.4747.
[3] Peter Chapman,et al. Environmental change in moorland landscapes , 2007 .
[4] N. R. Napolitano,et al. Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks , 2017, 1702.07675.
[5] Kirpal Nandra,et al. CANDELS MULTI-WAVELENGTH CATALOGS: SOURCE DETECTION AND PHOTOMETRY IN THE GOODS-SOUTH FIELD , 2013, 1308.4405.
[6] Roberto G. Abraham,et al. A CATALOG OF DETAILED VISUAL MORPHOLOGICAL CLASSIFICATIONS FOR 14,034 GALAXIES IN THE SLOAN DIGITAL SKY SURVEY , 2010, 1001.2401.
[7] O. Lahav,et al. THE CLUSTER LENSING AND SUPERNOVA SURVEY WITH HUBBLE: AN OVERVIEW , 2011, 1106.3328.
[8] Matthew A. Bershady,et al. The asymmetry of galaxies: physical morphology for nearby and high redshift galaxies , 1999 .
[9] Joeri van Leeuwen,et al. Applying Deep Learning to Fast Radio Burst Classification , 2018, The Astronomical Journal.
[10] A. Aniyan,et al. Classifying Radio Galaxies with the Convolutional Neural Network , 2017, 1705.03413.
[11] O. Lahav,et al. CLASH: accurate photometric redshifts with 14 HST bands in massive galaxy cluster cores , 2017, 1705.02265.
[12] Ping Guo,et al. Pulsar Candidate Identification with Artificial Intelligence Techniques , 2017, ArXiv.
[13] Santiago,et al. A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING , 2015, 1509.05429.
[14] L. Guzzo,et al. The Cosmic Evolution Survey (COSMOS): Overview* , 2006, astro-ph/0612305.
[15] Luca Maria Gambardella,et al. Max-pooling convolutional neural networks for vision-based hand gesture recognition , 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).
[16] Marc Huertas-Company,et al. Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available Bayesian automated classification , 2010, 1010.3018.
[17] A. Oemler,et al. Evolution of galaxies in clusters. I. ISIT photometry of Cl 0024 + 1654 and 3C 295 , 1978 .
[18] Pablo A. Estévez,et al. Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection , 2017, ArXiv.
[19] Sander Dieleman,et al. Rotation-invariant convolutional neural networks for galaxy morphology prediction , 2015, ArXiv.
[20] A. Finoguenov,et al. EARLY-TYPE GALAXIES AT z = 1.3. I. THE LYNX SUPERCLUSTER: CLUSTER AND GROUPS AT z = 1.3. MORPHOLOGY AND COLOR–MAGNITUDE RELATION , 2012, 1205.1785.
[21] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[22] E. al.,et al. The Sloan Digital Sky Survey: Technical summary , 2000, astro-ph/0006396.
[23] Evolution of Galaxy morphologies in Clusters , 2000, astro-ph/0008195.
[24] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[25] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[26] I. Smail,et al. The All-Wavelength Extended Groth Strip International Survey (AEGIS) Data Sets , 2006, astro-ph/0607355.
[27] J. Trump,et al. CANDELS VISUAL CLASSIFICATIONS: SCHEME, DATA RELEASE, AND FIRST RESULTS , 2014, 1401.2455.
[28] Jr.,et al. Evolution since z = 0.5 of the Morphology-Density Relation for Clusters of Galaxies , 1997, astro-ph/9707232.
[29] I. Smail,et al. Morphological Studies of the Galaxy Populations in Distant “Butcher-Oemler” Clusters with the Hubble Space Telescope. II. AC 103, AC 118, and AC 114 at z = 0.31 , 1997, astro-ph/9711019.
[30] R. Bouwens,et al. The Morphology-Density Relation in z ~ 1 Clusters , 2005, astro-ph/0501224.
[31] A. Dressler. Galaxy morphology in rich clusters: Implications for the formation and evolution of galaxies , 1980 .
[32] B. J. Weiner,et al. accepted to the Astrophysical Journal Preprint typeset using L ATEX style emulateapj v. 10/09/06 THE EVOLUTION OF GALAXY MERGERS AND MORPHOLOGY AT Z < 1.2 IN THE EXTENDED GROTH STRIP , 2007 .
[33] J. Laurence pritchard. Monthly Notices , 1941, The Journal of the Royal Aeronautical Society.
[34] H. Hoekstra,et al. THE GEMINI CLUSTER ASTROPHYSICS SPECTROSCOPIC SURVEY (GCLASS): THE ROLE OF ENVIRONMENT AND SELF-REGULATION IN GALAXY EVOLUTION AT z ∼ 1 , 2011, 1112.3655.
[35] H. D. S'anchez,et al. Improving galaxy morphologies for SDSS with Deep Learning , 2017, 1711.05744.
[36] M. Giavalisco,et al. The Great Observatories Origins Deep Survey: Initial results from optical and near-infrared imaging , 2003, astro-ph/0309105.
[37] M. Huertas-Company,et al. The morphological transformation of red sequence galaxies in clusters since z ∼ 1 , 2017, 1707.00751.
[38] C. Lintott,et al. Galaxy Zoo: Quantitative visual morphological classifications for 48 000 galaxies from CANDELS , 2016, 1610.03070.
[39] Christopher J. Miller,et al. THE XMM CLUSTER SURVEY: GALAXY MORPHOLOGIES AND THE COLOR–MAGNITUDE RELATION IN XMMXCS J2215.9 − 1738 AT z = 1.46 , 2009, 0903.1731.
[40] Christopher J. Shallue,et al. Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 , 2017, 1712.05044.
[41] Guoqiang Peter Zhang,et al. Neural networks for classification: a survey , 2000, IEEE Trans. Syst. Man Cybern. Part C.
[42] S. Ravindranath,et al. CANDELS: THE COSMIC ASSEMBLY NEAR-INFRARED DEEP EXTRAGALACTIC LEGACY SURVEY—THE HUBBLE SPACE TELESCOPE OBSERVATIONS, IMAGING DATA PRODUCTS, AND MOSAICS , 2011, 1105.3753.
[43] N. R. Tanvir,et al. Galaxy morphology to I = 25 mag in the Hubble Deep Field , 1996 .
[44] O. Fèvre,et al. A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images I. Method description , 2007, 0709.1359.
[45] J. Brinkmann,et al. The environmental dependence of the relations between stellar mass, structure, star formation and nuclear activity in galaxies , 2004, astro-ph/0402030.
[46] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[47] C. Lintott,et al. Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey , 2008, 0804.4483.
[48] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[49] R. Nichol,et al. The dependence of star formation history and internal structure on stellar mass for 105 low‐redshift galaxies , 2002, astro-ph/0205070.
[50] R. Pelló,et al. The morphological content of 10 EDisCS clusters at 0.5 < z < 0.8 , 2007, astro-ph/0701788.
[51] M. Irwin,et al. The UKIRT Infrared Deep Sky Survey (UKIDSS) , 2006, astro-ph/0604426.