Abstract:
Extracting information from stochastic fields is a ubiquitous task in science. However, from cosmology to biology, it tends to be done either through a power spectrum analysis, which is often too limited, or the use of neural networks, which require large training sets and lack interpretability.
I will present a new powerful tool called the “scattering transform” which stands nicely between the two extremes, and recent updates to extend this idea. I will use various examples in astrophysics and beyond to demonstrate its power, interpretability, and its advantage over traditional statistics.
