9 citations · 28 across the 17 of their papers we have counts for
6 papers · 1 filter
Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics
Tony Menzo, Alexander Roman, George T. Fleming +3
We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symb…
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone t…
Discovering Sparse Representations of Lie Groups with Machine Learning
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter,…
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks…
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in…
Uncertainties associated with GAN-generated datasets in high energy physics
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets a…