How the Edge of Our Galaxy Defies Known Physics

It's perhaps interesting to look at human psychology as cosmology in the sense that we perceive +- 3 semitones(~5%) to be acceptably in tune when listening to waveforms. Perhaps we have largely evolved to perceive the world through senses (physical sensors) that are easier to perceive, simply because the matter/energy we can perceive happens to resonate sensors comprised of matter (a sensor is performing its function when it is resonated by energy) and since dark matter/energy does not readily resonate much of anything we're used to perceiving with biological and mechanical sensors it's working on a longer wavelength where length correlates to dissonance along an axis of higher dimensional space. 

Perhaps the question is; what are these attractors that guide this 95%/5% split of "resonance/dissonance" (regular matter/dark matter and energy), or is there an attraction field that guides a correlation/interaction between some measurable form of dissonance and dark matter/energy correlated to their wavelengths. Perhaps this "attraction field body" serves as a sensor/mechanism that is resonated by dark matter/energy and thus holds the key in finding the correlation between parameters like wavelength/dissonance/space time tuning and emergent organization if the attraction field body is assumed to have a function that correlates to some measured emergent reverse entropy where the input is biased towards entropy/dissonance (dark matter/energy). 

So I think the solution will be discovered using machine learning categorization/clustering. The first step is to find predictive patterns in a dataset of galaxies with and without and varying levels of dark matter/energy, labeling them, and training ML to predict the amount of dark matter/energy of a galaxy based on data from all the available measurable components of a galaxy viewed through all types of telescopes. Dark matter/energy and likely other hidden features of our universe may emerge as an observable quantifiable data pattern in dimensionality-reduced data clustering where we can move the problem into the data science domain where statistical methods would likely reveal novel discoveries.