Encoding large scale cosmological structure with Generative Adversarial Networks
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In this article, a generative adversarial network (GAN) and an autoencoder (AE) are trained on images issued from two types of N-body simulations, namely 2D and 3D simulations.Abstract:
Recently a type of neural networks called Generative Adversarial Networks (GANs) has been proposed as a solution for fast generation of simulation-like datasets, in an attempt to bypass heavy computations and expensive cosmological simulations to run in terms of time and computing power. In the present work, we build and train a GAN to look further into the strengths and limitations of such an approach. We then propose a novel method in which we make use of a trained GAN to construct a simple autoencoder (AE) as a first step towards building a predictive model. Both the GAN and AE are trained on images issued from two types of N-body simulations, namely 2D and 3D simulations. We find that the GAN successfully generates new images that are statistically consistent with the images it was trained on. We then show that the AE manages to efficiently extract information from simulation images, satisfyingly inferring the latent encoding of the GAN to generate an image with similar large scale structures.read more
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Monthly Notices of the Royal Astronomical Society
TL;DR: The Monthly Notices as mentioned in this paper is one of the three largest general primary astronomical research publications in the world, published by the Royal Astronomical Society (RAE), and it is the most widely cited journal in astronomy.
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A Method for Weak Lensing Observations
TL;DR: In this article, a method for measuring the gravitational lensing induced distortion of distant background galaxies is proposed, where the authors locate the galaxies and measure a 2-component ''polarisation'' or ellipticity statistic whose expectation value should be proportional to the gravitational shear.
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Large-scale dark matter simulations
Raul E. Angulo,Oliver Hahn +1 more
TL;DR: In this article , a review of collisionless numerical simulations for the large-scale structure of the universe is provided, and the main set of equations solved by these simulations and their connection with General Relativity are discussed.
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Investigating cosmological GAN emulators using latent space interpolation
Andrius Tamosiunas,Andrius Tamosiunas,Hans A. Winther,Kazuya Koyama,David Bacon,Robert C. Nichol,Ben Mawdsley +6 more
TL;DR: This work trains a GAN to produce weak lensing convergence maps and dark matter overdensity field data for multiple redshifts, cosmological parameters, and modified gravity models, and applies the technique of latent space interpolation as a tool for understanding the feature space of the GAN algorithm.
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Survey2Survey: a deep learning generative model approach for cross-survey image mapping
Brandon Buncher,Awshesh Nath Sharma,Matias Carrasco Kind,Matias Carrasco Kind,Matias Carrasco Kind +4 more
TL;DR: While only an initial application, this method shows promise as a method for robustly expanding and improving the quality of optical survey data and provides a potential avenue for cross-band reconstruction.
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TL;DR: In this article, the authors present cosmological parameter results from the full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction.
Monthly Notices of the Royal Astronomical Society
TL;DR: The Monthly Notices as mentioned in this paper is one of the three largest general primary astronomical research publications in the world, published by the Royal Astronomical Society (RAE), and it is the most widely cited journal in astronomy.