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.
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), have been explored as alternatives to traditional simulations but face challenges with training instability and sparse data coverage. This study investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) for full-detector, whole-event heavy-ion collision simulations as a...
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...
Real-time data collection and analysis in large experimental facilities pose significant challenges across multiple domains, including high-energy physics, nuclear physics, and cosmology. Machine learning (ML)-based methods for real-time data compression have garnered substantial attention as a solution. In this talk, we will explore the use of deep neural networks in designing fast...
A demonstrator for separating events with a heavy flavor decay from background events in proton-proton collisions with the sPHENIX detector is presented. Due to data volume limitations, sPHENIX is capable of recording 10% of the minimum-bias collisions at RHIC using streaming readout in addition to its 15 kHz hardware trigger of rare events. This demonstrator will use 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...
The Electron-Ion Collider, a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in early 2030. Artificial intelligence and machine learning are being incorporated from the beginning at this facility and will continue to be used throughout all phases leading up to the experiments. In this talk, I will highlight a few examples of...
Measurement of jets and their substructure will provide valuable information about the underlying dynamics of hard-scattered quarks and gluons in Deep-Inelastic Scattering events. The ePIC Barrel Hadronic Calorimeter (BHCal) will be a critical tool for such measurements at the Electron-Ion Collider. By enabling the measurement of the neutral hadronic component of jets, the BHCal will...
In this talk, we present some results about EBIS beam intensity and RHIC luminosity online and offline optimization, using the machine learning packages GPTune and XGBoost.
Discussion and examples of Bayesian optimization, Reinforcement learning, and future planning for particle accelerators.
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.
advice from a PHENIX 2006-10 alumn and Hedge Fund co-founder
