Correlation classes on the landscape: To what extent is string theory predictive?

Keith R. Dienes, Michael Lennek

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In light of recent discussions of the string landscape, it is essential to understand the degree to which string theory is predictive. We argue that it is unlikely that the landscape as a whole will exhibit unique correlations amongst low-energy observables, but rather that different regions of the landscape will exhibit different overlapping sets of correlations. We then provide a statistical method for quantifying this degree of predictivity, and for extracting statistical information concerning the relative sizes and overlaps of the regions corresponding to these different correlation classes. Our method is robust and requires no prior knowledge of landscape properties, and can be applied to the landscape as a whole as well as to any relevant subset.

Original languageEnglish (US)
Article number106003
JournalPhysical Review D - Particles, Fields, Gravitation and Cosmology
Issue number10
StatePublished - Nov 16 2009

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Physics and Astronomy (miscellaneous)


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