A string landscape guide to soft SUSY breaking terms
Howard Baer, Vernon Barger, Shadman Salam, Dibyashree Sengupta

TL;DR
This paper explores how the string theory landscape influences the distribution of soft SUSY breaking terms, proposing a statistical framework that predicts non-universal scalar masses and their implications for particle physics phenomenology.
Contribution
It introduces a predictive landscape framework where soft SUSY terms scan independently, leading to natural explanations for non-universality and addressing SUSY flavor and CP problems.
Findings
Soft terms follow a power-law distribution favoring larger values.
Non-universality of scalar masses emerges as a natural feature.
Predictions for Higgs and sparticle masses inform LHC and dark matter searches.
Abstract
We examine several issues pertaining to statistical predictivity of the string theory landscape for weak scale supersymmetry (SUSY). We work within a predictive landscape wherein super-renormalizable terms scan while renormalizable terms do not. We require stringy naturalness wherein the likelihood of values for observables is proportional to their frequency within a fertile patch of landscape including the MSSM as low energy effective theory with a pocket-universe value for the weak scale nearby to its measured value in our universe. In the string theory landscape, it is reasonable that the soft terms enjoy a statistical power-law draw to large values, subject to the existence of atoms as we know them (atomic principle). We argue that gaugino masses, scalar masses and trilinear soft terms should each scan independently. In addition, the various scalars should scan independently of each…
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