# CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

@article{Mustafa2019CosmoGANCH, title={CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks}, author={Mustafa Mustafa and Deborah Bard and Wahid Bhimji and Zarija Lukic and Rami Al-Rfou and Jan Michael Kratochvil}, journal={Computational Astrophysics and Cosmology}, year={2019}, volume={6}, pages={1-13} }

Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic… Expand

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#### References

SHOWING 1-10 OF 84 REFERENCES

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

- Physics, Mathematics
- 2017

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in high energy particle physics by applying a novel Generative Adversarial Network… Expand

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images

- Computer Science, Physics
- AAAI
- 2017

This work considers variations on conditional variational autoencoder and introduces a new adversarial objective for training of conditional generative networks that suggests a reliable alternative to the acquisition of expensive high quality observations for generating the calibration data needed by the next generation of cosmological surveys. Expand

BourGAN: Generative Networks with Metric Embeddings

- Computer Science, Mathematics
- NeurIPS
- 2018

This paper addresses the mode collapse for generative adversarial networks (GANs) by embedding subsamples of the dataset from an arbitrary metric space into the l2 space, while preserving their pairwise distance distribution. Expand

Fast cosmic web simulations with generative adversarial networks

- Computer Science, Physics
- 2018

This paper demonstrates the application of a machine learning technique called Generative Adversarial Networks (GAN) to learn models that can efficiently generate new, physically realistic realizations of the cosmic web, and finds a good match for the correlation matrix of full Pk$P_{k}$ range for 100 Mpc data and of small scales for 500 Mpc, with ∼20% disagreement for large scales. Expand

Denoising Weak Lensing Mass Maps with Deep Learning

- Physics, Computer Science
- Physical Review D
- 2019

An image-to-image translation method with conditional adversarial networks (CANs), which learn efficient mapping from an input noisy weak lensing map to the underlying noise field, and improves the cosmological constraints by using observational data from ongoing and upcoming galaxy imaging surveys. Expand

Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters.

- Computer Science, Medicine
- Physical review letters
- 2018

A deep neural network-based generative model is introduced to enable high-fidelity, fast, electromagnetic calorimeter simulation and opens the door to a new era of fast simulation that could save significant computing time and disk space, while extending the reach of physics searches and precision measurements at the LHC and beyond. Expand

Are GANs Created Equal? A Large-Scale Study

- Computer Science, Mathematics
- NeurIPS
- 2018

A neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures finds that most models can reach similar scores with enough hyperparameter optimization and random restarts, suggesting that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. Expand

Sampling Generative Networks: Notes on a Few Effective Techniques

- Computer Science
- ArXiv
- 2016

Several techniques for effectively sampling and visualizing the latent spaces of generative models are introduced and two new techniques for deriving attribute vectors are demonstrated: bias-corrected vectors with data replication and synthetic vectors withData augmentation. Expand

Generative Adversarial Nets

- Computer Science
- NIPS
- 2014

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a… Expand

Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit

- Computer Science, Physics
- ArXiv
- 2017

The ability to better recover detailed features from low-signal-to-noise and low angular resolution imaging data significantly increases the ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope and the Hubble and James Webb space telescopes. Expand