### Abstract

The realization that string theory gives rise to a huge landscape of vacuum solutions has recently prompted a statistical approach towards extracting phenomenological predictions from string theory. Unfortunately, for most classes of string models, direct enumeration of all solutions is not computationally feasible and thus statistical studies must resort to other methods in order to extract meaningful information. In this paper, we discuss some of the issues that arise when attempting to extract statistical correlations from a large data set to which our computational access is necessarily limited. Our main focus is the problem of "floating correlations." As we discuss, this problem is endemic to investigations of this type and reflects the fact that not all physically distinct string models are equally likely to be sampled in any random search through the landscape, thereby causing statistical correlations to float as a function of sample size. We propose several possible methods that can be used to overcome this problem, and we show through explicit examples that these methods lead to correlations and statistical distributions which are not only stable as a function of sample size, but which differ significantly from those which would have been naïvely apparent from only a partial data set.

Original language | English (US) |
---|---|

Article number | 026008 |

Journal | Physical Review D - Particles, Fields, Gravitation and Cosmology |

Volume | 75 |

Issue number | 2 |

DOIs | |

State | Published - Jan 29 2007 |

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### ASJC Scopus subject areas

- Physics and Astronomy(all)
- Nuclear and High Energy Physics
- Mathematical Physics

### Cite this

*Physical Review D - Particles, Fields, Gravitation and Cosmology*,

*75*(2), [026008]. https://doi.org/10.1103/PhysRevD.75.026008

**Fighting the floating correlations : Expectations and complications in extracting statistical correlations from the string theory landscape.** / Dienes, Keith R; Lennek, Michael.

Research output: Contribution to journal › Article

*Physical Review D - Particles, Fields, Gravitation and Cosmology*, vol. 75, no. 2, 026008. https://doi.org/10.1103/PhysRevD.75.026008

}

TY - JOUR

T1 - Fighting the floating correlations

T2 - Expectations and complications in extracting statistical correlations from the string theory landscape

AU - Dienes, Keith R

AU - Lennek, Michael

PY - 2007/1/29

Y1 - 2007/1/29

N2 - The realization that string theory gives rise to a huge landscape of vacuum solutions has recently prompted a statistical approach towards extracting phenomenological predictions from string theory. Unfortunately, for most classes of string models, direct enumeration of all solutions is not computationally feasible and thus statistical studies must resort to other methods in order to extract meaningful information. In this paper, we discuss some of the issues that arise when attempting to extract statistical correlations from a large data set to which our computational access is necessarily limited. Our main focus is the problem of "floating correlations." As we discuss, this problem is endemic to investigations of this type and reflects the fact that not all physically distinct string models are equally likely to be sampled in any random search through the landscape, thereby causing statistical correlations to float as a function of sample size. We propose several possible methods that can be used to overcome this problem, and we show through explicit examples that these methods lead to correlations and statistical distributions which are not only stable as a function of sample size, but which differ significantly from those which would have been naïvely apparent from only a partial data set.

AB - The realization that string theory gives rise to a huge landscape of vacuum solutions has recently prompted a statistical approach towards extracting phenomenological predictions from string theory. Unfortunately, for most classes of string models, direct enumeration of all solutions is not computationally feasible and thus statistical studies must resort to other methods in order to extract meaningful information. In this paper, we discuss some of the issues that arise when attempting to extract statistical correlations from a large data set to which our computational access is necessarily limited. Our main focus is the problem of "floating correlations." As we discuss, this problem is endemic to investigations of this type and reflects the fact that not all physically distinct string models are equally likely to be sampled in any random search through the landscape, thereby causing statistical correlations to float as a function of sample size. We propose several possible methods that can be used to overcome this problem, and we show through explicit examples that these methods lead to correlations and statistical distributions which are not only stable as a function of sample size, but which differ significantly from those which would have been naïvely apparent from only a partial data set.

UR - http://www.scopus.com/inward/record.url?scp=33846623803&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33846623803&partnerID=8YFLogxK

U2 - 10.1103/PhysRevD.75.026008

DO - 10.1103/PhysRevD.75.026008

M3 - Article

AN - SCOPUS:33846623803

VL - 75

JO - Physical review D: Particles and fields

JF - Physical review D: Particles and fields

SN - 0556-2821

IS - 2

M1 - 026008

ER -