List of Deep Learning methods for Cosmology
-
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Nathanaël Perraudin, Michaël Defferrard, Tomasz Kacprzak, Raphael Sgier
https://arxiv.org/abs/1810.12186
https://github.com/SwissDataScienceCenter/DeepSphere -
DeepCMB: Lensing Reconstruction of the Cosmic Microwave Background with Deep Neural Networks
J. Caldeira, W. L. K. Wu, B. Nord, C. Avestruz, S. Trivedi, K. T. Story
https://arxiv.org/abs/1810.01483 -
Analysis of Cosmic Microwave Background with Deep Learning
Siyu He, Siamak Ravanbakhsh, Shirley Ho
https://openreview.net/forum?id=B15uoOyvz -
Creating Virtual Universes Using Generative Adversarial Networks
Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan Kratochvil
https://arxiv.org/abs/1706.02390 https://github.com/MustafaMustafa/cosmoGAN -
Fast Cosmic Web Simulations with Generative Adversarial Networks
Andres C. Rodriguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphael Sgier, Janis Fluri, Thomas Hofmann, Alexandre Réfrégier
https://arxiv.org/abs/1801.09070 -
Cosmological model discrimination with Deep Learning
Jorit Schmelzle, Aurelien Lucchi, Tomasz Kacprzak, Adam Amara, Raphael Sgier, Alexandre Réfrégier, Thomas Hofmann
https://arxiv.org/abs/1707.05167 -
Learning to Predict the Cosmological Structure Formation
Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos
https://arxiv.org/abs/1811.06533 -
Estimating Cosmological Parameters from the Dark Matter Distribution
Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos
https://arxiv.org/abs/1711.02033 -
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee
https://arxiv.org/abs/1808.04728 -
Non-Gaussian information from weak lensing data via deep learning
Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu, Zoltán Haiman
https://arxiv.org/abs/1802.01212 -
Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks Yashar D. Hezaveh, Laurence Perreault Levasseur, Philip J. Marshall
https://arxiv.org/abs/1708.08842 -
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
Laurence Perreault Levasseur, Yashar D. Hezaveh, Risa H. Wechsler
https://arxiv.org/abs/1708.08843 -
CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
Francois Lanusse, Quanbin Ma, Nan Li, Thomas E. Collett, Chun-Liang Li, Siamak Ravanbakhsh, Rachel Mandelbaum, Barnabas Poczos
https://arxiv.org/abs/1703.02642
https://github.com/McWilliamsCenter/CMUDeepLens