Searching the landscape of flux vacua with genetic algorithms

Cole, Alex; Schachner, Andreas; Shiu, Gary

doi:10.1007/jhep11(2019)045

Cited by 74 publications

(75 citation statements)

References 102 publications

(169 reference statements)

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“…However, a full understanding of this question requires detailed (numerical) studies of the distributions of flux vacua which is well beyond the scope of the present work. For estimates of the number of vacua as a function of the flux superpotential and the string coupling see [40][41][42][43][44].…”

Section: Jhep10(2020)015mentioning

confidence: 99%

“…Given that LVS models can be realised for natural values of the vacuum expectation value of the flux superpotential, |W 0 | ∼ O(1 − 10), while KKLT models can be constructed only via tuning |W 0 | to exponentially small values (similar considerations about tuning of the underlying parameters apply also to perturbatively stabilised vacua), we tend to conclude that the distribution of the scale of supersymmetry breaking seems to be logarithmic. However, more detailed studies are needed in order to find a precise definite answer to this important question (see [40][41][42][43][44] for initial studies on the determination of the number of vacua as a function of |W 0 | and g s ).…”

Section: Jhep10(2020)015mentioning

confidence: 99%

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Moduli stabilisation and the statistics of SUSY breaking in the landscape

Broeckel

Cicoli

Maharana

et al. 2020

J. High Energ. Phys.

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The statistics of the supersymmetry breaking scale in the string landscape has been extensively studied in the past finding either a power-law behaviour induced by uniform distributions of F-terms or a logarithmic distribution motivated by dynamical supersymmetry breaking. These studies focused mainly on type IIB flux compactifications but did not systematically incorporate the Kähler moduli. In this paper we point out that the inclusion of the Kähler moduli is crucial to understand the distribution of the supersymmetry breaking scale in the landscape since in general one obtains unstable vacua when the F-terms of the dilaton and the complex structure moduli are larger than the F- terms of the Kähler moduli. After taking Kähler moduli stabilisation into account, we find that the distribution of the gravitino mass and the soft terms is power-law only in KKLT and perturbatively stabilised vacua which therefore favour high scale supersymmetry. On the other hand, LVS vacua feature a logarithmic distribution of soft terms and thus a preference for lower scales of supersymmetry breaking. Whether the landscape of type IIB flux vacua predicts a logarithmic or power-law distribution of the supersymmetry breaking scale thus depends on the relative preponderance of LVS and KKLT vacua.

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Section: Jhep10(2020)015mentioning

confidence: 99%

Section: Jhep10(2020)015mentioning

confidence: 99%

Moduli stabilisation and the statistics of SUSY breaking in the landscape

Broeckel

Cicoli

Maharana

et al. 2020

J. High Energ. Phys.

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“…The associated data can be found in [50], where it is the third model in the list of elliptic fibrations. In the context of flux vacua this manifold is also considered in [22,26,47], and more recently in [51,52].…”

Section: The One-parameter Octic X 8amentioning

confidence: 99%

On flux vacua and modularity

Schimmrigk

2020

J. High Energ. Phys.

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Geometric modularity has recently been conjectured to be a characteristic feature of flux vacua with W = 0. This paper provides support for the conjecture by computing motivic modular forms in a direct way for several string compactifications for which such vacua are known to exist. The analysis of some Calabi-Yau manifolds which do not admit supersymmetric flux vacua shows that the reverse of the conjecture does not hold.

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“…It is necessary to provide the ANN with concrete examples to be able to identify certain patterns among the different fluxes, which in turn would lead to some stable or unstable extremal point in moduli space. This is the reason to use genetic algorithms previous to adapting the neural network [9,10,31,39,42,43]. Since there is not a single example of a stable dS, it is possible that the network does not identify such cases and in consequence it will not learn how to construct them.…”

Section: Introductionmentioning

confidence: 99%

Testing swampland conjectures with machine learning

et al. 2020

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We consider Type IIB compactifications on an isotropic torus $$T^6$$T6 threaded by geometric and non geometric fluxes. For this particular setup we apply supervised machine learning techniques, namely an artificial neural network coupled to a genetic algorithm, in order to obtain more than sixty thousand flux configurations yielding to a scalar potential with at least one critical point. We observe that both stable AdS vacua with large moduli masses and small vacuum energy as well as unstable dS vacua with small tachyonic mass and large energy are absent, in accordance to the refined de Sitter conjecture. Moreover, by considering a hierarchy among fluxes, we observe that perturbative solutions with small values for the vacuum energy and moduli masses are favored, as well as scenarios in which the lightest modulus mass is much smaller than the corresponding AdS vacuum scale. Finally we apply some results on random matrix theory to conclude that the most probable mass spectrum derived from this string setup is that satisfying the Refined de Sitter and AdS scale conjectures.

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Searching the landscape of flux vacua with genetic algorithms

Cited by 74 publications

References 102 publications

Moduli stabilisation and the statistics of SUSY breaking in the landscape

Moduli stabilisation and the statistics of SUSY breaking in the landscape

On flux vacua and modularity

Testing swampland conjectures with machine learning

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