A fine-tuning measure is defined from the eigenvalues of a rescaled Fisher information matrix on parameter space, with a geometric interpretation as the pullback of the Euclidean metric from observable space.
Measures of fine tuning
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abstract
Fine-tuning criteria are frequently used to place upper limits on the masses of superpartners in supersymmetric extensions of the standard model. However, commonly used prescriptions for quantifying naturalness have some important shortcomings. Motivated by this, we propose new criteria for quantifying fine tuning that can be used to place upper limits on superpartner masses with greater fidelity. In addition, our analysis attempts to make explicit the assumptions implicit in quantifications of naturalness. We apply our criteria to the minimal supersymmetric extension of the standard model, and we find that the scale of supersymmetry breaking can be larger than previous methods indicate.
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Develops a Bayesian naturalness measure and evaluates its implications for the Minimal 4D Composite Higgs model and extensions via global fits and collider bounds.
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Naturalness and Fisher Information
A fine-tuning measure is defined from the eigenvalues of a rescaled Fisher information matrix on parameter space, with a geometric interpretation as the pullback of the Euclidean metric from observable space.
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The Landscape of Composite Higgs Models
Develops a Bayesian naturalness measure and evaluates its implications for the Minimal 4D Composite Higgs model and extensions via global fits and collider bounds.