This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Naturalness_(physics)
00:01:44 1 Overview
00:04:04 2 Introduction
00:06:27 3 Naturalness and the gauge hierarchy problem
00:11:44 4 Naturalness, supersymmetry and the little hierarchy
00:17:32 5 Status of naturalness and the little hierarchy
00:23:27 6 See also
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– Socrates
SUMMARY
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In physics, naturalness is the property that the dimensionless ratios between free parameters or physical constants appearing in a physical theory should take values “of order 1” and that free parameters are not fine-tuned. That is, a natural theory would have parameter ratios with values like 2.34 rather than 234000 or 0.000234.
The requirement that satisfactory theories should be “natural” in this sense is a current of thought initiated around the 1960s in particle physics. It is an aesthetic criterion, not a physical one, that arises from the seeming non-naturalness of the standard model and the broader topics of the hierarchy problem, fine-tuning, and the anthropic principle. However it does tend to suggest a possible area of weakness or future development for current theories such as the Standard Model, where some parameters vary by many orders of magnitude, and which require extensive “fine-tuning” of their current values of the models concerned. The concern is that it is not yet clear whether these seemingly exact values we currently recognize, have arisen by chance (based upon the anthropic principle or similar) or whether they arise from a more advanced theory not yet developed, in which these turn out to be expected and well-explained, because of other factors not yet part of particle physics models.
The concept of naturalness is not always compatible with Occam’s razor, since many instances of “natural” theories have more parameters than “fine-tuned” theories such as the Standard Model. Naturalness in physics is closely related to the issue of fine-tuning, and over the past decade many scientists argued that the principle of naturalness is a specific application of Bayesian statistics.
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