Data science applications to string theory

Ruehle, Fabian

doi:10.1016/j.physrep.2019.09.005

Cited by 129 publications

(141 citation statements)

References 97 publications

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“…In the growing subject (see Ref. [26] for a recent summary of data science application to string theory), the idea of equating a holographic spacetime with neural network [11,12,16,17,[27][28][29] may be intertwined with machine learning string landscapes initiated by Refs. [30][31][32][33].…”

Section: Discussionmentioning

confidence: 99%

Deep learning and AdS/QCD

2020

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We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra. A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are holographically calculated. We adopt the experimentally measured spectra of ρ and a 2 mesons as training data, and perform a supervised machine learning which determines concretely a bulk metric and a dilaton profile of an AdS/QCD model. Our deep learning (DL) architecture is based on the AdS/DL correspondence [K. Hashimoto, S. Sugishita, A. Tanaka, and A. Tomiya, Phys. Rev. D 98, 046019 (2018)] where the deep neural network is identified with the emergent bulk spacetime.

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Section: Discussionmentioning

confidence: 99%

Deep learning and AdS/QCD

2020

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“…Our discussion is far from comprehensive, and the reader is directed to Sutton and Barto's book [ 42 ] for a pedagogical and extensive introduction to the subject, and to the vast literature on reinforcement learning for more details. We also recommend [20], and the insightful review by F. Ruehle [ 43 ] for a discussion of reinforcement learning applied to problems in string theory. In particular, chapter 8 of [43] explains most of the concepts we need in this paper.…”

Section: Settingmentioning

confidence: 99%

“…We also recommend [20], and the insightful review by F. Ruehle [ 43 ] for a discussion of reinforcement learning applied to problems in string theory. In particular, chapter 8 of [43] explains most of the concepts we need in this paper.…”

Section: Settingmentioning

confidence: 99%

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Explore and Exploit with Heterotic Line Bundle Models

Larfors

Schneider

2020

Fortschritte der Physik

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We use deep reinforcement learning to explore a class of heterotic SU(5) GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds. We perform several experiments where A3C agents are trained to search for such models. These agents significantly outperform random exploration, in the most favourable settings by a factor of 1700 when it comes to finding unique models. Furthermore, we find evidence that the trained agents also outperform random walkers on new manifolds. We conclude that the agents detect hidden structures in the compactification data, which is partly of general nature. The experiments scale well with h (1,1) , and may thus provide the key to model building on CICYs with large h (1,1) . 1 We will refer to such compactifications as 'realistic models' below.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.

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“…Implementation of different computational methods in high-energy physics research has increased notably in the last few years. See e.g [31][32][33][34][35][36][37][38][39][40][41]…”

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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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Data science applications to string theory

Cited by 129 publications

References 97 publications

Deep learning and AdS/QCD

Deep learning and AdS/QCD

Explore and Exploit with Heterotic Line Bundle Models

Testing swampland conjectures with machine learning

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