# The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM

## TL;DR

This paper introduces the Reduced Wavelet Scattering Transform (RWST), a compact statistical tool derived from the Wavelet Scattering Transform, designed to characterize complex non-Gaussian structures in the interstellar medium for better comparison between observations and simulations.

## Contribution

The paper develops the RWST, a reduced and physically interpretable statistical description that efficiently captures non-linear structures in the ISM without relying on specific priors.

## Key findings

- RWST uses fewer than 100 coefficients for 6 scales and 8 angles.
- RWST successfully compares different processes like fractional Brownian motions, MHD simulations, and Herschel observations.
- RWST enables quantitative analysis, physical inference, and realistic field synthesis.

## Abstract

The interstellar medium (ISM) is a complex non-linear system governed by gravity and magneto-hydrodynamics, as well as radiative, thermodynamical, and chemical processes. Our understanding of it mostly progresses through observations and numerical simulations, and a quantitative comparison between these two approaches requires a generic and comprehensive statistical description. The goal of this paper is to build such a description, with the purpose to permit an efficient comparison independent of any specific prior or model. We start from the Wavelet Scattering Transform (WST), a low-variance statistical description of non-Gaussian processes, developed in data science, that encodes long-range interactions through a hierarchical multiscale approach based on the Wavelet transform. We perform a reduction of the WST through a fit of its angular dependencies, allowing to gather most of the information it contains into a few components whose physical meanings are identified, and that describe, e.g., isotropic and anisotropic behaviours. The result of this paper is the Reduced Wavelet Scattering Transform (RWST), a statistical description with a small number of coefficients that characterizes complex structures arising from non-linear phenomena, free from any specific prior. The RWST coefficients encode moments of order up to four, have reduced variances, and quantify the couplings between scales. To show the efficiency and generality of this description, we apply it successfully to three kinds of processes: fractional Brownian motions, MHD simulations, and Herschel observations in a molecular cloud. With fewer than 100 coefficients when probing 6 scales and 8 angles on 256*256 maps, we were able with the RWST to perform quantitative comparisons, to infer relevant physical properties, and to produce realistic synthetic fields.