Pantheon SEMPARIS Le serveur des séminaires parisiens Paris

Statut Confirmé
Domaines hep-th
Date Jeudi 21 Septembre 2023
Heure 11:45
Institut IHP
Salle Grisvard (314)
Nom de l'orateur Ashmore
Prenom de l'orateur Anthony
Addresse email de l'orateur
Institution de l'orateur LPTHE
Titre Machine learning for geometry and string compactifications
Résumé Understanding Calabi-Yau metrics and hermitian Yang-Mills connections has long been a challenge in mathematics and theoretical physics. These geometric objects play a crucial role in constructing realistic models of particle physics in string theory. However, with no closed-form expressions for them, we are unable to compute basic quantities in top-down string models, such as particle masses and couplings. Breakthroughs in machine learning have opened a new path to tackle this problem. After recalling the relationship between these geometric ingredients and 4d effective field theory, I will review recent progress in using machine learning to calculate these metrics and connections numerically. Finally, I will highlight how this newly available geometric data can be used, including studying the spectrum of Laplace-type operators on a Calabi-Yau in the presence of a background gauge field.
Numéro de preprint arXiv
Fichiers attachés
  • Ashmore_slidesIHP.pdf (5466661 bytes) OPEN

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