Conveners
Computing, Machine Learning, & AI
- Tanner Mengel (University of Tennessee (UTK))
- Jakub Kvapil (Los Alamos National Laboratory)
This talk will provide an overview of applications of artificial intelligence at RHIC for a variety of purposes ranging from data-taking to physics analysis. Applications ongoing and envisioned for the upcoming EIC will also be discussed.
Reconstructing jets in heavy collisions has always required dealing with the challenges of a high background environment. Traditional techniques, such as the area based method, suffered from poor resolution at low momenta due to the large fluctuating background there. In recent years, the resolution has been improved by using machine learning to estimate the background. While machine learning...
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by interaction with the quark gluon plasma. Modifications of the hard substructure of jets can be explored using modern data-driven techniques. In this study, we use a machine learning approach to identify jet quenching amounts. Jet showering processes, both with and...
MultiFold is a machine-learning based technique that can correct for detector effects for multiple observables in an unbinned manner. In this talk, we discuss how MultiFold works, highlight its applications in several experiments, and introduce resources available to get started on using it.
OmniFold, the full phase space application of MultiFold, is an unbinned way of correcting multiple observables for detector effects simultaneously using machine learning. As these dependencies are typically addressed in a binned, observable-by-observable fashion, OmniFold presents a novel alternative. In this talk, we present the OmniFold method and a direct application of it to jet-level STAR data.
