Journal Article

Testing swampland conjectures with machine learning

Damian, Cesar, Bizet, Nana Cabo, Mayorga Peña, Damián Kaloni, Montañez-Barrera, J. A., Loaiza-Brito, Oscar

2020

Overview

We consider Type IIB compactifications on an isotropic torus T 6 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.

• Publisher: Springer Berlin Heidelberg,Springer,Springer Nature B.V,SpringerOpen
• Subject: Advertising executives / Algorithms / Artificial neural networks / Astronomy / Astrophysics and Cosmology / Critical point / Elementary Particles / Fluxes / Genetic algorithms / Hadrons / Heavy Ions / Machine learning / Matrix theory / Measurement Science and Instrumentation / Neural networks / Nuclear Energy / Nuclear Physics / Physics / Physics and Astronomy / Quantum Field Theories / Quantum Field Theory / Regular Article - Theoretical Physics / String Theory / Toruses