An improved cosmological parameter inference scheme motivated by deep learning

[1]  Daniel J. Hsu,et al.  Non-Gaussian information from weak lensing data via deep learning , 2018, ArXiv.

[2]  B. Yanny,et al.  Dark Energy Survey year 1 results: Cosmological constraints from galaxy clustering and weak lensing , 2017, Physical Review D.

[3]  Thomas Hofmann,et al.  Cosmological model discrimination with Deep Learning , 2017, 1707.05167.

[4]  Daniel J. Hsu,et al.  Do dark matter halos explain lensing peaks , 2016, 1609.03973.

[5]  P. Schneider,et al.  KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing , 2016, 1606.05338.

[6]  C. B. D'Andrea,et al.  Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data , 2016, 1603.05040.

[7]  Morgan May,et al.  Sample variance in weak lensing: How many simulations are required? , 2016, 1601.06792.

[8]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[9]  M. May,et al.  Cosmology constraints from the weak lensing peak counts and the power spectrum in CFHTLenS data , 2014, 1412.0757.

[10]  Martin Kilbinger,et al.  Cosmology with cosmic shear observations: a review , 2014, Reports on progress in physics. Physical Society.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  S. Bridle,et al.  Cosmic Discordance: Are Planck CMB and CFHTLenS weak lensing measurements out of tune? , 2014, 1408.4742.

[13]  H. Hoekstra,et al.  CFHTLenS: cosmological constraints from a combination of cosmic shear two-point and three-point correlations , 2014, 1404.5469.

[14]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[15]  Naoki Yoshida,et al.  STATISTICAL AND SYSTEMATIC ERRORS IN THE MEASUREMENT OF WEAK-LENSING MINKOWSKI FUNCTIONALS: APPLICATION TO THE CANADA–FRANCE–HAWAII LENSING SURVEY , 2013, 1312.5032.

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  G. Meylan,et al.  Weak lensing mass map and peak statistics in Canada-France-Hawaii Telescope Stripe 82 survey , 2013, 1311.1319.

[18]  Morgan May,et al.  Cosmology with Minkowski functionals and moments of the weak lensing convergence field , 2013, 1309.4460.

[19]  S. Krughoff,et al.  The effective number density of galaxies for weak lensing measurements in the LSST project , 2013, 1305.0793.

[20]  P. Schneider,et al.  The cosmological information of shear peaks: beyond the abundance , 2013, 1301.5001.

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  R. Nichol,et al.  Euclid Definition Study Report , 2011, 1110.3193.

[23]  University of Cambridge,et al.  Probing Cosmology with Weak Lensing Minkowski Functionals , 2011, 1109.6334.

[24]  Morgan May,et al.  Probing cosmology with weak lensing peak counts , 2009, 0907.0486.

[25]  J. P. Dietrich,et al.  Cosmology with the shear-peak statistics , 2009, 0906.3512.

[26]  Eduardo Serrano,et al.  LSST: From Science Drivers to Reference Design and Anticipated Data Products , 2008, The Astrophysical Journal.

[27]  Masahiro Takada,et al.  Three-point correlations in weak lensing surveys: Model predictions and applications , 2003, astro-ph/0304034.

[28]  M. White,et al.  Simulating Weak Lensing by Large-Scale Structure , 2003, astro-ph/0303555.

[29]  Tony Lindeberg,et al.  Scale-Space for Discrete Signals , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[31]  A. I. Salvador,et al.  Cosmological Constraints from Galaxy Clustering and Weak Lensing , 2018 .

[32]  J. Galloway.  A Review of the , 1901 .