Abstract

Patterns and complex textures are ubiquitous in astronomical data but challenging to quantify. I will present a new powerful statistic called the “scattering transform”. It borrows ideas from convolutional neural nets (CNNs) while retaining the advantages of traditional statistics. As an example, I will show its application to weak lensing cosmology, where it outperforms classic statistics and is on a par with CNNs. I will also show interesting visual interpretations of the scattering statistics and possible extensions of this “mathematical neural network” idea. I argue that the scattering transform provides a powerful new approach in cosmology and beyond.

Related papers:

https://arxiv.org/abs/2112.01288

https://arxiv.org/abs/2103.09247

https://arxiv.org/abs/2006.08561



Zoom Link: https://pitp.zoom.us/j/91612161747?pwd=bnQrVmo4ZjBjaUdQMDBNZGhFS2NPQT09

Details

Talk Number PIRSA:21120020
Speaker Profile Sihao Cheng