LHC String Phenomenology
Gordon L. Kane, Piyush Kumar, Jing Shao
TL;DR
This work tackles the deeper LHC inverse problem by proposing a framework to map semi-realistic string vacua to LHC signatures and to distinguish among string constructions using early LHC data. It demonstrates that different classes of string vacua (notably KKLT and Large Volume) plus eight string-motivated constructions yield finite, overlapping yet distinguishable signature footprints, which can be separated with about $5\,fb^{-1}$ of data and refined with more statistics. The approach relies on correlations between high-scale moduli stabilization, SUSY-breaking mediation, and low-energy soft-term patterns, translating microscopic theory into experimentally testable collider observables. The results provide a pragmatic path to connect string theory with collider data and motivate expanding the catalogue of vacua and signatures to sharpen discrimination in future analyses.
Abstract
We argue that it is possible to address the deeper LHC Inverse Problem, to gain insight into the underlying theory from LHC signatures of new physics. We propose a technique which may allow us to distinguish among, and favor or disfavor, various classes of underlying theoretical constructions using (assumed) new physics signals at the LHC. We think that this can be done with limited data $(5-10 fb^{-1})$, and improved with more data. This is because of two reasons -- a) it is possible in many cases to reliably go from (semi)realistic microscopic string construction to the space of experimental observables, say, LHC signatures. b) The patterns of signatures at the LHC are sensitive to the structure of the underlying theoretical constructions. We illustrate our approach by analyzing two promising classes of string compactifications along with six other string-motivated constructions. Even though these constructions are not complete, they illustrate the point we want to emphasize. We think that using this technique effectively over time can eventually help us to meaningfully connect experimental data to microscopic theory.
