Events at Physics

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Events on Wednesday, March 1st, 2023

Time: 12:15 pm
Place: Chamberlin 4274 or online
Abstract: We will be discussing the article, Creation of inclusive spaces with astromimicry. We will go over a brief summary and welcome attendees who have not had a chance to read the article.

GREAT IDEAS stands for Group for Reading, Educating, And Talking about Inclusion, Diversity, Equity, & Advocacy in Science. It is a multimedia reading group dedicated to amplifying the experiences of underrepresented groups in science and academia in order to become better advocates for our peers. GREAT IDEAS is open to everyone (students/ faculty/ staff/ etc), and all are welcome and encouraged to engage with the material and contribute to the discussions. To keep a welcoming and safe environment for everyone, we ask that everyone understand and adhere to our community guidelines for the discussions. If you would like to submit an article for a future GREAT IDEAS discussion, you can do so on this form.
Host: GMaWiP and Climate and Diversity Committee (contact Jessie Thwaites or R. Sassella with questions)
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Theory Seminar (High Energy/Cosmology)
Machine Learning for String Compactifications
Time: 1:00 pm
Place: Chamberlin 5280
Speaker: Anthony Ashmore, U. Chicago
Abstract: The mysterious nature of Calabi-Yau metrics and hermitian Yang-Mills connections has been a persistent challenge in mathematics and theoretical physics for decades. These elusive geometric objects play a critical role in deriving semi-realistic models of particle physics from string theory. However, with no explicit expressions for them, we are left unable to compute basic quantities in top-down string models, such as particle masses and couplings. Recent breakthroughs in machine learning have opened up a new avenue for tackling this problem. In this seminar, we will explore the potential of machine learning for computing these elusive objects. Starting with a review of their relationship to effective field theories, we will then delve into the latest progress in using machine learning to calculate Calabi-Yau metrics and hermitian Yang-Mills connections numerically. Finally, we will give examples of practical applications of this new data, including a test of the so-called "swampland distance conjecture".
Host: George Wojcik
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