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Move over neural networks! – A new method for cosmological inference

30 Jul 2020, 16:50 UTC
Move over neural networks! – A new method for cosmological inference
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Title: A New Approach to Observational Cosmology Using the Scattering TransformAuthors: Sihao Cheng, Yuan-Sen Ting, Brice Ménard, Joan BrunaFirst Author’s Institution: Department of Physics and Astronomy, The Johns Hopkins University, 3400 N Charles Street, Baltimore, MD 21218, USAStatus: Submitted to arXivNeural networks have seen a lot of hype in astronomy and cosmology recently (even just on this site! See these three bites). However, it may be that the neural networks used to classify images in typical machine learning applications are overkill. To quote the authors of today’s paper, “the cosmological density field is not as complex as random images of rabbits.” Today’s authors propose using a method called the “scattering transform” to take advantage of the best parts of neural networks with none of the limitations.
Non-Gaussianity InsanityThe standard cosmological lore states that early on the universe underwent a phase of inflation that is responsible for laying down the initial conditions of the large-scale structure we see in the universe today. These initial conditions were very nearly Gaussian, or white noise like static you might see on old TVs. The fluctuations of this noise produced the initial seeds of structure formation (for more, see these two bites about the Planck ...

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