Descending into the Modular Bootstrap

The modular bootstrap has been a powerful tool for carving out the landscape of allowed two-dimensional conformal field theories (CFTs).  In this colloquium, I describe a complementary approach to standard modular bootstrap bounds: using modern machine learning strategies to actively search for CFT spectra that yield a valid torus partition function.  Using insights from statistical inference and a custom singular-value-based optimizer, I present evidence for an obstruction to finding CFTs with small central charge and large spectral gaps, and I speculate on what this might imply for the structure of the CFT landscape.  Along the way, I reflect on "centaur" approaches to theoretical physics, where human physicists and artificial intelligence collaborate to explore spaces of theories that would be difficult to navigate alone.

Biography

Jesse Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics.  He joined the MIT Physics Department in 2010, and he is currently a Professor in the MIT Center for Theoretical Physics - a Leinweber Institute. In 2020, he became the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions. Thaler is currently on sabbatical in Paris, contemplating the future of AI+Physics from the city's research institutes and neighborhood cafés.