6 papers
AI and the Research-Education Environment of Physics
Savannah Thais, Koji Hashimoto, David S. Berman +6
In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy P…
Reconstructing conformal field theoretical compositions with Transformers
Haotian Cao, Garrett Merz, Kyle Cranmer +1
We study the use of transformers to reconstruct the compositions of tensor products of two-dimensional rational conformal field theories (RCFTs) based on their low-energy spectra.…
Multimodal Datasets with Controllable Mutual Information
Raheem Karim Hashmani, Garrett W. Merz, Helen Qu +2
We introduce a framework for generating highly multimodal datasets with explicitly calculable mutual information (MI) between modalities. This enables the construction of benchmark…
Recurrent Features of Amplitudes in Planar Super Yang-Mills Theory
Tianji Cai, François Charton, Kyle Cranmer +3
The planar three-gluon form factor for the chiral stress tensor operator in planar maximally supersymmetric Yang-Mills theory is an analog of the Higgs-to-three-gluon scattering am…
Self-Supervised Learning Strategies for Jet Physics
Patrick Rieck, Kyle Cranmer, Etienne Dreyer +5
We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simu…
Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory
Tianji Cai, Garrett W. Merz, François Charton +4
We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to th…