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[1] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[2] B. Nord,et al. DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks , 2018, Astron. Comput..
[3] G. W. Pratt,et al. XXIV. Cosmology from Sunyaev-Zeldovich cluster counts , 2015, 1502.01597.
[4] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] M. Becker,et al. ON THE ACCURACY OF WEAK-LENSING CLUSTER MASS RECONSTRUCTIONS , 2010, 1011.1681.
[6] Thomas Hofmann,et al. Cosmological constraints with deep learning from KiDS-450 weak lensing maps , 2019, Physical Review D.
[7] M. Halpern,et al. Optical design of the atacama cosmology telescope and the millimeter bolometric array camera. , 2006, Applied optics.
[8] G. Efstathiou,et al. Cosmology with the pairwise kinematic SZ effect: calibration and validation using hydrodynamical simulations , 2017, 1712.05714.
[9] Francesco Visin,et al. A guide to convolution arithmetic for deep learning , 2016, ArXiv.
[10] Patrick van der Smagt,et al. CNN-based Segmentation of Medical Imaging Data , 2017, ArXiv.
[11] Adrian T. Lee,et al. Measurements of the Temperature and E-mode Polarization of the CMB from 500 Square Degrees of SPTpol Data , 2017, 1707.09353.
[12] C. B. D'Andrea,et al. Weak-lensing analysis of SPT-selected galaxy clusters using Dark Energy Survey Science Verification data , 2018, Monthly Notices of the Royal Astronomical Society.
[13] Harvard,et al. Effects of Galaxy Formation on Thermodynamics of the Intracluster Medium , 2007, astro-ph/0703661.
[14] S. Kay,et al. An application of machine learning techniques to galaxy cluster mass estimation using the MACSIS simulations , 2018, Monthly Notices of the Royal Astronomical Society.
[15] Edward J. Wollack,et al. The Simons Observatory: science goals and forecasts , 2018, Journal of Cosmology and Astroparticle Physics.
[16] J. J. Bock,et al. BICEP2 and Keck array: upgrades and improved beam characterization , 2014, Astronomical Telescopes and Instrumentation.
[17] M. Lueker,et al. MASS CALIBRATION AND COSMOLOGICAL ANALYSIS OF THE SPT-SZ GALAXY CLUSTER SAMPLE USING VELOCITY DISPERSION σv AND X-RAY YX MEASUREMENTS , 2014, 1407.2942.
[18] A. Lewis,et al. Efficient computation of CMB anisotropies in closed FRW models , 1999, astro-ph/9911177.
[19] Lindsay J. King,et al. Cosmology with the cluster mass function: mass estimators and shape systematics in large weak lensing surveys , 2009, 0901.3434.
[20] Jorge Moreno,et al. Deep learning predictions of galaxy merger stage and the importance of observational realism , 2019, Monthly Notices of the Royal Astronomical Society.
[21] A. Kravtsov,et al. A UNIVERSAL MODEL FOR HALO CONCENTRATIONS , 2014, 1407.4730.
[22] David N. Spergel,et al. The Atacama Cosmology Telescope: Sunyaev-Zel'dovich selected galaxy clusters at 148 GHz from three seasons of data , 2013, 1301.0816.
[23] J. E. Ruhl,et al. COSMOLOGICAL CONSTRAINTS FROM GALAXY CLUSTERS IN THE 2500 SQUARE-DEGREE SPT-SZ SURVEY , 2016, 1603.06522.
[24] R. Nichol,et al. Euclid Definition Study Report , 2011, 1110.3193.
[25] Danica J. Sutherland,et al. A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS , 2014, 1410.0686.
[26] David N. Spergel,et al. The Atacama Cosmology Telescope: Dynamical masses for 44 SZ-selected galaxy clusters over 755 square degrees , 2015, 1512.00910.
[27] Federico Nati,et al. Evidence of lensing of the cosmic microwave background by dark matter halos. , 2014, Physical review letters.
[28] P. A. R. Ade,et al. The SPTpol Extended Cluster Survey , 2019, The Astrophysical Journal Supplement Series.
[29] Edward J. Wollack,et al. THE ATACAMA COSMOLOGY TELESCOPE: DYNAMICAL MASSES AND SCALING RELATIONS FOR A SAMPLE OF MASSIVE SUNYAEV–ZEL'DOVICH EFFECT SELECTED GALAXY CLUSTERS , 2012, 1201.0991.
[30] R. C. Smith,et al. Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters , 2018, Monthly Notices of the Royal Astronomical Society.
[31] Klaus Dolag,et al. Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning , 2019, The Astrophysical Journal.
[32] Adrian T. Lee,et al. Galaxy Clusters Selected via the Sunyaev–Zel’dovich Effect in the SPTpol 100-square-degree Survey , 2019, The Astronomical Journal.
[33] David N. Spergel,et al. The Atacama Cosmology Telescope: The Two-season ACTPol Sunyaev–Zel’dovich Effect Selected Cluster Catalog , 2017, 1709.05600.
[34] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[35] Christopher Joseph Pal,et al. The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.
[36] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[37] Qingjie Liu,et al. Road Extraction by Deep Residual U-Net , 2017, IEEE Geoscience and Remote Sensing Letters.
[38] Stephen Marshall,et al. Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.
[39] J. Koppenhoefer,et al. Weak lensing analysis of SZ-selected clusters of galaxies from the SPT and Planck surveys , 2013, 1310.6744.
[40] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[41] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[42] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] H. Hoekstra,et al. The Canadian Cluster Comparison Project: detailed study of systematics and updated weak lensing masses , 2015, 1502.01883.
[44] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[45] Prabhat,et al. CosmoFlow: Using Deep Learning to Learn the Universe at Scale , 2018, SC18: International Conference for High Performance Computing, Networking, Storage and Analysis.
[46] David Donovan,et al. Weighing the giants– V. Galaxy cluster scaling relations , 2016, 1606.03407.
[47] The Planck Collaboration. The Scientific Programme of Planck , 2006 .
[48] Alexey Vikhlinin,et al. CHANDRA CLUSTER COSMOLOGY PROJECT III: COSMOLOGICAL PARAMETER CONSTRAINTS , 2008, 0812.2720.
[49] Joop Schaye,et al. The scatter and evolution of the global hot gas properties of simulated galaxy cluster populations , 2016, 1606.04545.
[50] J. E. Ruhl,et al. Cluster Cosmology Constraints from the 2500 deg2 SPT-SZ Survey: Inclusion of Weak Gravitational Lensing Data from Magellan and the Hubble Space Telescope , 2018, The Astrophysical Journal.
[51] M. Meneghetti,et al. CLASH-VLT: The mass, velocity-anisotropy, and pseudo-phase-space density profiles of the z = 0.44 galaxy cluster MACS J1206.2-0847 , 2013, 1307.5867.
[52] F. Vazza,et al. Turbulent gas motions in galaxy cluster simulations: the role of smoothed particle hydrodynamics viscosity , 2005 .
[53] M. Lueker,et al. A MEASUREMENT OF SECONDARY COSMIC MICROWAVE BACKGROUND ANISOTROPIES FROM THE 2500 SQUARE-DEGREE SPT-SZ SURVEY , 2014, 1408.3161.
[54] Xin Liu,et al. Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era , 2019, ArXiv.
[55] Edward J. Wollack,et al. Advanced ACTPol Cryogenic Detector Arrays and Readout , 2015, 1510.02809.
[56] Daniel George,et al. Deep Neural Networks to Enable Real-time Multimessenger Astrophysics , 2016, ArXiv.
[57] B. Póczos,et al. A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters , 2019, The Astrophysical Journal.
[58] Mark Halpern,et al. CMB-S4 Science Case, Reference Design, and Project Plan , 2019, 1907.04473.
[59] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[60] H. Hoekstra,et al. Sunyaev–Zel’dovich effect and X-ray scaling relations from weak lensing mass calibration of 32 South Pole Telescope selected galaxy clusters , 2017, Monthly Notices of the Royal Astronomical Society.
[61] P. A. R. Ade,et al. GALAXY CLUSTERS DISCOVERED WITH A SUNYAEV–ZEL'DOVICH EFFECT SURVEY , 2008, 0810.1578.
[62] S. W. Allen,et al. New constraints on dark energy from the observed growth of the most X-ray luminous galaxy clusters , 2007, 0709.4294.
[63] Emanuele Usai,et al. Deep Learning the Morphology of Dark Matter Substructure , 2019 .
[64] M. Lueker,et al. A MEASUREMENT OF GRAVITATIONAL LENSING OF THE COSMIC MICROWAVE BACKGROUND BY GALAXY CLUSTERS USING DATA FROM THE SOUTH POLE TELESCOPE , 2014, 1412.7521.
[65] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[66] P. A. R. Ade,et al. SPT-3G: a next-generation cosmic microwave background polarization experiment on the South Pole telescope , 2014, Astronomical Telescopes and Instrumentation.
[67] Adrian T. Lee,et al. The 10 Meter South Pole Telescope , 2009, 0907.4445.
[68] F. Marinacci,et al. A Deep Learning Approach to Galaxy Cluster X-Ray Masses , 2018, The Astrophysical Journal.
[69] G. W. Pratt,et al. The universal galaxy cluster pressure profile from a representative sample of nearby systems (REXCESS) and the Y-SZ-M-500 relation , 2009, 0910.1234.
[70] Klaus Dolag,et al. SZ effects in the Magneticum Pathfinder Simulation: Comparison with the Planck, SPT, and ACT results , 2015, 1509.05134.
[71] R. B. Barreiro,et al. Planck 2018 results , 2018, Astronomy & Astrophysics.
[72] J. Mohr,et al. Galaxy kinematics and mass calibration in massive SZE-selected galaxy clusters toz = 1.3 , 2017, Monthly Notices of the Royal Astronomical Society.
[73] P. A. R. Ade,et al. Mass Calibration of Optically Selected DES Clusters Using a Measurement of CMB-cluster Lensing with SPTpol Data , 2018, The Astrophysical Journal.
[74] Takahiro Nishimichi,et al. The mass–richness relation of optically selected clusters from weak gravitational lensing and abundance with Subaru HSC first-year data , 2019, Publications of the Astronomical Society of Japan.
[75] Paolo Conconi,et al. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series , 2012 .
[76] J. Mohr,et al. SZE observables, pressure profiles and centre offsets in Magneticum simulation galaxy clusters , 2016, 1612.05266.
[77] Donald W. Sweeney,et al. LSST Science Book, Version 2.0 , 2009, 0912.0201.
[78] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.