Pantheon SEMPARIS Le serveur des séminaires parisiens Paris

Status Confirmed
Seminar Series RENC-THEO
Subjects hep-th
Date Thursday 21 September 2023
Time 11:45
Institute IHP
Seminar Room Grisvard (314)
Speaker's Last Name Ashmore
Speaker's First Name Anthony
Speaker's Email Address
Speaker's Institution LPTHE
Title Machine learning for geometry and string compactifications
Abstract 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.
arXiv Preprint Number
  • Ashmore_slidesIHP.pdf (5466661 bytes) OPEN

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