Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps
  using Deep Learning   Cosmic Microwave Background (CMB) has been a cornerstone in many cosmology
experiments and studies since it was discovered back in 1964. Traditional
computational models like CAMB that are used for generating CMB temperature
anisotropy maps are extremely resource intensive and act as a bottleneck in
cosmology experiments that require a large amount of CMB data for analysis. In
this paper, we present a new approach to the generation of CMB temperature maps
using a specific class of neural networks called Generative Adversarial Network
(GAN). We train our deep generative model to learn the complex distribution of
CMB maps and efficiently generate new sets of CMB data in the form of 2D
patches of anisotropy maps without losing much accuracy. We limit our
experiment to the generation of 56$^{\circ}$ and 112$^{\circ}$ square patches
of CMB maps. We have also trained a Multilayer perceptron model for estimation
of baryon density from a CMB map, we will be using this model for the
performance evaluation of our generative model using diagnostic measures like
Histogram of pixel intensities, the standard deviation of pixel intensity
distribution, Power Spectrum, Cross power spectrum, Correlation matrix of the
power spectrum and Peak count. We show that the GAN model is able to
efficiently generate CMB samples of multiple sizes and is sensitive to the
cosmological parameters corresponding to the underlying distribution of the
data. The primiary advantage of this method is the exponential reduction in the
computational time needed to generate the CMB data, the GAN model is able to
generate the samples within seconds as opposed to hours required by the CAMB
package with an acceptable value to error and loss of information. We hope that
future iterations of this methodology will replace traditional statistical
methods of CMB data generation and help in large scale cosmological
experiments.

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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
9
The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio