Contrast data mining for the MSSM from strings

Parr, Erik; Vaudrevange, Patrick K.S.

doi:10.1016/j.nuclphysb.2020.114922

Cited by 19 publications

(13 citation statements)

References 67 publications

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“…It is important to note that RL and GAs are qualitatively different from the more standard supervised and unsupervised learning techniques, which have also been recently used in the exploration of the heterotic string landscape [88][89][90][91].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Intelligent Explorations of the String Theory Landscape

Constantin¹

2022

Preprint

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The goal of identifying the Standard Model of particle physics and its extensions within string theory has been one of the principal driving forces in string phenomenology. Recently, the incorporation of artificial intelligence in string theory and certain theoretical advancements have brought to light unexpected solutions to mathematical hurdles that have so far hindered progress in this direction. In this review we focus on model building efforts in the context of the E 8 × E 8 heterotic string compactified on smooth Calabi-Yau threefolds and discuss several areas in which machine learning is expected to make a difference.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Intelligent Explorations of the String Theory Landscape

Constantin¹

2022

Preprint

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“…Furthermore, the matter spectrum together with their transformation properties under the available symmetries are also fixed by the compactification. In this kind of models, it has been shown that the exact matter spectrum of the SM can be achieved, including quarks and leptons and their mixings [143][144][145][146]. Among the symmetries of these string models, one identifies their discrete flavor symmetries (which technically correspond to the outer automorphisms of the Narain space group associated with the orbifold).…”

Section: Eclectic Flavor Symmetriesmentioning

confidence: 99%

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation: A Snowmass White Paper

Almumin¹,

Chen²,

Cheng³

et al. 2022

Preprint

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The neutrino sector offers one of the most sensitive probes of new physics beyond the Standard Model of Particle Physics (SM). The mechanism of neutrino mass generation is still unknown. The observed suppression of neutrino masses hints at a large scale, conceivably of the order of the scale of a Grand Unified Theory (GUT), a unique feature of neutrinos that is not shared by the charged fermions. The origin of neutrino masses and mixing is part of the outstanding puzzle of fermion masses and mixings, which is not explained in the SM. Flavor model building for both quark and lepton sectors is important in order to gain a better understanding of the origin of the structure of mass hierarchy and flavor mixing, which constitute the dominant fraction of the SM parameters.Recent activities in neutrino flavor model building based on non-Abelian discrete flavor symmetries and modular flavor symmetries have been shown to be a promising direction to explore. The emerging models provide a framework that has a significantly reduced number of undetermined parameters in the flavor sector. In addition, such framework affords a novel origin of CP violation from group theory, due to the intimate connection between physical CP transformation and group theoretical properties of non-Abelian discrete groups.Model building based on non-Abelian discrete flavor symmetries and their modular variants enables the particle physics community to interpret the current and anticipated upcoming data from neutrino experiments. Non-Abelian discrete flavor symmetries and their modular variants can result from compactification of a higher-dimensional theory. Pursuit of flavor model building based on such frameworks thus also provides the connection to possible UV completions, in particular to string theory. We emphasize the importance of constructing models in which the uncertainties of theoretical predictions are smaller than, or at most compatible with, the error bars of measurements in neutrino experiments. While there exist proof-of-principle versions of bottom-up models in which the theoretical uncertainties are under control, it is remarkable that the key ingredients of such constructions were discovered first in top-down model building. We outline how a successful unification of bottom-up and top-down ideas and techniques may guide us towards a new era of precision flavor model building in which future experimental results can give us crucial insights in the UV completion of the SM.

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“…Artificial neural networks (ANNs) are algorithms inspired in the biological learning process. The use of machine learning techniques to solve classification problems has attracted more attention in the last years [33,37,39,[50][51][52][53]. The machine learning techniques allows to search in large amount of data for specific patterns and thus, it provides an exhaustive check in a short time.…”

Section: Appendix A: Artificial Neural Networkmentioning

confidence: 99%

“…The machine learning techniques allows to search in large amount of data for specific patterns and thus, it provides an exhaustive check in a short time. 7 7 For instance, in [37] using data mining the authors are able to look for suitable heterotic compactifications that selects an appropriate line bun-In the following we describe in simple terms, the structure of an ANN. Each neuron in the hidden layer is connected with all the neurons in the neighboring clusters through a weight factor.…”

Section: Appendix A: Artificial Neural Networkmentioning

confidence: 99%

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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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Contrast data mining for the MSSM from strings

Cited by 19 publications

References 67 publications

Intelligent Explorations of the String Theory Landscape

Intelligent Explorations of the String Theory Landscape

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation: A Snowmass White Paper

Testing swampland conjectures with machine learning

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