Fast automated analysis of strong gravitational lenses with convolutional neural networks
暂无分享,去创建一个
[1] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[2] M. Bartelmann,et al. Isothermal elliptical gravitational lens models. , 1994 .
[3] S. Warren,et al. Semilinear Gravitational Lens Inversion , 2003, astro-ph/0302587.
[4] Cambridge,et al. A Bayesian analysis of regularized source inversions in gravitational lensing , 2006, astro-ph/0601493.
[5] A. Bolton,et al. Accepted for publication in The Astrophysical Journal Preprint typeset using L ATEX style emulateapj v. 10/09/06 THE SLOAN LENS ACS SURVEY. V. THE FULL ACS STRONG-LENS SAMPLE 1 , 2022 .
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] A. Lefor,et al. A systematic review of strong gravitational lens modeling software , 2012, 1206.4382.
[8] Cosmology,et al. THE SL2S GALAXY-SCALE LENS SAMPLE. III. LENS MODELS, SURFACE PHOTOMETRY, AND STELLAR MASSES FOR THE FINAL SAMPLE , 2013, 1307.4764.
[9] Aaron Roodman,et al. THE THIRD GRAVITATIONAL LENSING ACCURACY TESTING (GREAT3) CHALLENGE HANDBOOK , 2013, 1308.4982.
[10] R. Fergus,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[11] Cosmology,et al. THE SL2S GALAXY-SCALE LENS SAMPLE. V. DARK MATTER HALOS AND STELLAR IMF OF MASSIVE EARLY-TYPE GALAXIES OUT TO REDSHIFT 0.8 , 2014, 1410.1881.
[12] D. Coe,et al. LENS MODELS AND MAGNIFICATION MAPS OF THE SIX HUBBLE FRONTIER FIELDS CLUSTERS , 2014, 1405.0222.
[13] T. Collett. THE POPULATION OF GALAXY–GALAXY STRONG LENSES IN FORTHCOMING OPTICAL IMAGING SURVEYS , 2015, 1507.02657.
[14] Zoubin Ghahramani,et al. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference , 2015, ArXiv.
[15] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[16] Christian Borgs,et al. Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes , 2016, Proceedings of the National Academy of Sciences.
[17] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[18] Karl Glazebrook,et al. Finding strong lenses in CFHTLS using convolutional neural networks , 2017, 1704.02744.
[19] Barnabás Póczos,et al. Enabling Dark Energy Science with Deep Generative Models of Galaxy Images , 2016, AAAI.
[20] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[21] C. Lintott,et al. Galaxy Zoo: morphological classifications for 120 000 galaxies in HST legacy imaging , 2016, 1610.03068.
[22] N. R. Napolitano,et al. Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks , 2017, 1702.07675.
[23] B. Póczos,et al. CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding , 2017, Monthly Notices of the Royal Astronomical Society.