Conveners
Collider 1
- Andrew Ivanov (Kansas State University (US))
Optimal kinematic observables are often defined in specific frames and then approximated at the reconstruction level. We show how multi-dimensional unfolding methods allow us to reconstruct these observables in their proper rest frame and in a probabilistically faithful way. We illustrate our approach with a measurement of a CP-phase in the top Yukawa coupling. Our method makes use of key...
Graph Neural Networks have emerged as a powerful tool for operating on graph-structured data, facilitating the exploration of non-Euclidean physics data. In this talk, I will discuss the application of GNNs in a supervised scenario where we explore its potential to improve high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based...
In this presentation, I will elucidate the diverse array of cutting-edge computing resources available for unrestricted use. Noteworthy examples include the BeoCat High-Performance Computing (HPC) system at Kansas State University, the formidable Pete Supercomputer at Oklahoma State University, and the highly efficient BeoShock HPC system at Wichita State University. These resources grant...
