Using Wavelet Decomposition to Determine the Dimension of Structures from Projected Images
Mesoscale structures can often be described as fractional dimensional across a wide range of scales. We consider a $γ$ dimensional measure embedded in an $N$ dimensional space and discuss how to determine its dimension, both in $N$ dimensions and projected into $D$ dimensions. It is a highly non-trivial problem to decode the original geometry from lower dimensional projection of a high-dimensional measure. The projections are space-feeling, the popular box-counting techniques do not apply, and the Fourier methods are contaminated by aliasing effects. In the present paper we demonstrate that under the "Copernican hypothesis'' that we are not observing objects from a special direction, projection in a wavelet basis is remarkably simple: the wavelet power spectrum of a projected $γ$ dimensional measure is $P_j \propto 2^{-jγ}$. This holds regardless of the embedded dimension, $N$, and the projected dimension, $D$. This approach could have potentially broad applications in data sciences where a typically sparse matrix encodes lower dimensional information embedded in an extremely high dimensional field and often measured in projection to a low dimensional space. Here, we apply this method to JWST and Chandra observations of the nearby supernova Cas A. We find that the emissions can be represented by projections of mesoscale substructures with fractal dimensions varying from $γ= 1.7$ for the warm CO layer observed by JWST, up to $γ= 2.5$ for the hot X-ray emitting gas layer in the supernova remnant. The resulting power law indicates that the emission is coming from a fractal dimensional mesoscale structure likely produced by magneto-hydrodynamical instabilities in the expanding supernova shell.