Wavelet-based cross-correlation analysis of structure scaling in turbulent clouds
T.G. Arshakian, V. Ossenkopf

TL;DR
This paper introduces a wavelet-based cross-correlation method to analyze and compare the scale-dependent structure and correlations in molecular cloud maps, revealing chemical and physical transitions.
Contribution
The paper develops the WWCC method for scale-dependent analysis of turbulence in molecular clouds, calibrated with artificial maps and applied to real observations.
Findings
Confirmed chemical similarity of $^{13}$CO and C$^{18}$O across scales
Identified a chemical transition scale at ~7 pc in HCN maps
Detected systematic offsets indicating large-scale density gradients
Abstract
We propose a statistical tool to compare the scaling behaviour of turbulence in pairs of molecular cloud maps. Using artificial maps with well defined spatial properties, we calibrate the method and test its limitations to ultimately apply it to a set of observed maps. We develop the wavelet-based weighted cross-correlation (WWCC) method to study the relative contribution of structures of different sizes and their degree of correlation in two maps as a function of spatial scale, and the mutual displacement of structures in the molecular cloud maps. We test the WWCC for circular structures having a single prominent scale and fractal structures showing a self-similar behavior without prominent scales. Observational noise and a finite map size limit the scales where the cross-correlation coefficients and displacement vectors can be reliably measured. For fractal maps containing many…
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