A new vocabulary for textures and its cosmological application

Textures and patterns are ubiquitous in astronomical data but challenging for quantitative analysis. I will present a new tool to characterize textures, called the "scattering transform”. It borrows ideas from convolutional neural nets and shares advantages of traditional statistical estimators. As an example, I will show its application to weak lensing data for constraining cosmological parameters and compare its performance with other methods.

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